International Technology Alliance In Network and ...nis-ita.org/Legacy/files/plans/IPP2006_TechVolume.pdf · Klein Associates, Inc. Small/Medium Enterprises Klein Associates, Inc

International Technology AllianceIn

Network and Information Sciences

Initial Program Plan

Applicable Period: May 12th 2006 – May 11th 2007Editor: Dinesh Verma, IBM {[email protected]}

Table of Contents

1. Introduction ....................................................................................................................12. Alliance Overview..........................................................................................................2

2.1. Academic Members of the Alliance................................................................................32.2. Industrial Members of Alliance ......................................................................................42.3. Government Members of the Alliance ...........................................................................52.4. Ways of Working ............................................................................................................5

3. Technical Area Overview ...............................................................................................6

3.1. Technical Area 1: Network Theory.................................................................................63.2. Technical Area 2: Security Across a System of Systems...............................................73.3. Technical Area 3: Sensor Information Processing and Delivery ....................................83.4. Technical Area 4: Distributed Coalition Planning and Decision Making.....................103.5. Cross Area Project: Project 13......................................................................................123.6. Project to Technical Area Mapping ..............................................................................13

4. Project 1: Theoretical Foundations for Analysis and Design of Wireless and Sensor Networks14

4.1. Project Summary/Research Issues Addressed ..............................................................144.2. Technical Approach ......................................................................................................144.3. Relevance to US/UK Military Visions .........................................................................204.4. Collaborations and Staff Rotations...............................................................................204.5. Relation to DoD/MoD and Industry Research .............................................................204.6. Research Milestones.....................................................................................................204.7. Budget .......................................................................... Error! Bookmark not defined.4.8. References ....................................................................................................................21

5. Project 2: Interoperability of Wireless Networks and Systems ....................................23

5.1. Project Summary/Research Issues Addressed ..............................................................235.2. Technical Approach ......................................................................................................245.3. Relevance to US/UK Military Visions .........................................................................295.4. Collaborations and Staff Rotations...............................................................................295.5. Relation to DoD/MoD and Industry Research .............................................................305.6. Research Milestones.....................................................................................................305.7. Budget by Organization................................................ Error! Bookmark not defined.5.8. References ....................................................................................................................32

6. Project 3: Biologically Inspired Self Management of Networks..................................33

6.1. Project Summary/Research Issues Addressed ..............................................................336.2. Technical Approach ......................................................................................................336.3. Relevance to US/UK Military Visions .........................................................................376.4. Collaborations and Staff Rotations...............................................................................376.5. Relation to DoD/MoD and Industry Research .............................................................376.6. Research Milestones.....................................................................................................376.7. Budget By Organization ............................................... Error! Bookmark not defined.6.8. References ....................................................................................................................38

7. Project 4: Policy based Security Management .............................................................39

7.1. Project Summary/Research Issues Addressed ..............................................................397.2. Technical Approach ......................................................................................................407.3. Relevance to US/UK Military Visions .........................................................................487.4. Collaborations and Staff Rotations...............................................................................487.5. Relation to DoD/MoD and Industry Research .............................................................497.6. Research Milestones.....................................................................................................497.7. Budget .......................................................................... Error! Bookmark not defined.

8. Project 5: Energy Efficient Security Architectures and Infrastructures........................51

8.1. Project Summary/Research Issues Addressed ..............................................................518.2. Technical Approach ......................................................................................................528.3. Relevance to US/UK Military Visions .........................................................................548.4. Collaborations and Staff Rotations...............................................................................548.5. Relation to DoD/MoD and Industry Research .............................................................548.6. Research Milestones.....................................................................................................548.7. Budget .......................................................................... Error! Bookmark not defined.8.8. References: ...................................................................................................................55

9. Project 6: Trust and Risk Management in Dynamic Coalition Environments.............56

9.1. Project Summary/Research Issues Addressed ..............................................................569.2. Technical Approach ......................................................................................................579.3. Relevance to US/UK Military Visions .........................................................................609.4. Collaborations and Staff Rotations...............................................................................609.5. Relation to DoD/MoD and Industry Research .............................................................609.6. Research Milestones.....................................................................................................619.7. Budget by Organization................................................ Error! Bookmark not defined.9.8. References: ...................................................................................................................61

10. Project 7: Quality of Information in Sensor Data.........................................................62

10.1. Project Summary/Research Issues Addressed ............................................................6210.2. Technical Approach ....................................................................................................6210.3. Relevance to US/UK Military Visions .......................................................................6710.4. Collaborations and Staff Rotations.............................................................................6710.5. Relation to DoD/MoD and Industry Research ...........................................................6710.6. Research Milestones...................................................................................................6710.7. Budget by Organization.............................................. Error! Bookmark not defined.10.8. References ..................................................................................................................69

11. Project 8: Task Oriented Deployment of Sensor Data Infrastructure ..........................70

11.1. Project Summary/Research Issues Addressed ............................................................7011.2. Technical Approach ....................................................................................................7011.3. Relevance to US/UK Military Visions........................................................................7511.4. Collaborations and Staff Rotations .............................................................................7511.5. Relation to DoD/MoD and Industry Research............................................................7511.6. Research Milestones ...................................................................................................7511.7. Budget by Organization.............................................. Error! Bookmark not defined.11.8. References ..................................................................................................................76

12. Project 9: Complexity Management of Sensor Data Infrastructure..............................77

12.1. Project Summary/Research Issues Addressed ............................................................7712.2. Technical Approach ....................................................................................................78

12.3. References ..................................................................................................................8112.4. Relevance to US/UK Military Visions .......................................................................8212.5. Collaborations and Staff Rotations.............................................................................8312.6. Relation to DoD/MoD and Industry Research ...........................................................8312.7. Research Milestones...................................................................................................8312.8. Budget by Organization.............................................. Error! Bookmark not defined.

13. Project 10: Mission Adaptive Collaboration.................................................................85

13.1. Project Summary/Research Issues Addressed ............................................................8513.2. Technical Approach ....................................................................................................8613.3. Relevance to US/UK Military Visions .......................................................................9013.4. Collaborations and Staff Rotations.............................................................................9013.5. Relation to DoD/MoD and Industry Research ...........................................................9013.6. References ..................................................................................................................9113.7. Research Milestones...................................................................................................9113.8. Budget by Organization.............................................. Error! Bookmark not defined.

14. Project 11: Command Process Transformation and Analysis ......................................93

14.1. Project Summary/Research Issues Addressed ............................................................9314.2. Technical Approach ....................................................................................................9314.3. Relevance to US/UK Military Visions .......................................................................9814.4. Collaborations and Staff Rotations.............................................................................9814.5. Relation to DoD/MoD and Industry Research ...........................................................9814.6. References ..................................................................................................................9814.7. Research Milestones...................................................................................................9914.8. Budget by Organization.............................................. Error! Bookmark not defined.

15. Project 12: Shared Situation Awareness and the Semantic Battlespace Infosphere ..101

15.1. Project Summary/Research Issues Addressed ..........................................................10115.2. Technical Approach ..................................................................................................10215.3. Relevance to US/UK Military Visions .....................................................................10615.4. Collaborations and Staff Rotations...........................................................................10715.5. Relation to DoD/MoD and Industry Research .........................................................10815.6. References ................................................................................................................10815.7. Research Milestones.................................................................................................10915.8. Budget by Organization.............................................. Error! Bookmark not defined.

16. Project 13: Cross-Project Activities............................................................................ 112

16.1. Project Summary/Research Objectives .................................................................... 11216.2. Technical Approach .................................................................................................. 11216.3. Technical Deliverables ............................................................................................. 11316.4. Budget by Organization.............................................. Error! Bookmark not defined.

17. Schedule of Meetings and Conferences...................................................................... 11418. First Year Budget for Projects and Organizations ........ Error! Bookmark not defined.

mailto:[email protected]






International Technology Alliance – Initial Program Plan

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1. Introduction

ITA (International Technology Alliance) is a collaborative partnership between the US ArmyResearch Laboratories, the UK Ministry of Defense and a consortium of industries and universities in USand UK. The goal of the alliance is to foster a new way of working among researchers in both nations.The members of the alliance seek to break down barriers, build relationships, develop mutualunderstanding and work in partnership to develop technology for the US and UK military.

ITA has taken the best features of national collaborative programs in the two countries, namely theUK Defence Technology Centres and US Army's Collaborative Technology Alliances, and applied theminternationally. The focus of the alliance is to conduct research in an area central to future coalitionmilitary operations - Network and Information Sciences.

The alliance will address research issue in four technical areas: Network Theory, Security Across aSystem of Systems; Sensor Information Processing & Delivery and Distributed Coalition Planning &Decision Making. It will illustrate the application of the synergistic combination of these technical areasto network centric warfare and network enabling capabilities in support of all missions required of today'smilitary forces including humanitarian support, peacekeeping, and full combat operations in any kind ofterrain, but especially in complex and urban terrain.

The alliance will create a critical mass of private sector and Government scientists and engineersfocused on solving the military technology challenges in network centric warfare for both countries aswell as supporting and stimulating dual-use applications of this research and technology to benefitcommercial use.

This document describes the IPP (Initial Program Plan) for the alliance, which describes the projectsand technical activities to be undertaken for the first year of its existence. The projects and tasks describedin the IPP are to be undertaken between the period of May 12th, 2006 and May 11th, 2007.

This document is structured as follows. In Section 2 of the document, an overview of the alliance andthe manner in which it will conduct its fundamental research is described. Section 3 provides an overviewof the research to be conducted in each of the four technical areas. This is followed by thirteen sections,each section describing a project to be undertaken in this program, and the activities to be performed inthe first year. Section 17 provides the schedule of meetings that will be conducted in the first year. Finally,Section 18 provides an organizational break-down of the first year budget for the alliance.


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2. Alliance OverviewSuccess in a vision like ITA requires three key competencies, (i) adventurous thinking which results

in blue-sky high-risk high-reward research activities, (ii) military knowledge and experience in deliveringmilitary systems to the US and UK and (iii) ability to coordinate and bridge the gap between fundamentalresearch and the military transition. The research philosophy for ITA envisions the adventurous thinkingto emanate primarily from the universities, the military expertise coming from the two governments andindustrial members such as The Boeing Company, Honeywell and LogicaCMG, with IBM, as the researchfacilitator, providing the bridge between the two components leveraging its large research organizationand commercial transition expertise.

In addition to UK MoD and US ARL researchers, the alliance includes foremost academic researchersfrom eight US and seven UK top-tier universities. Our industrial alliance members fall into twocategories: large system integrators with extensive defense and commercial presence (The BoeingCompany, Honeywell and LogicaCMG) and small/medium enterprises (SMEs) with renowned expertisein the specialized fields that the ITA program is focused upon.

Figure 1. Alliance Members

Consortium LeadIBM Corp


System IntegratorsThe Boeing CompanyHoneywell Aerospace ElectronicSystemsBBNT Solutions LLC

System IntegratorsThe Boeing CompanyHoneywell Aerospace ElectronicSystemsBBNT Solutions LLC

Small/Medium Enterprises

Klein Associates, Inc.

Small/Medium Enterprises

Klein Associates, Inc.

UniversitiesCarnegie Mellon UniversityColumbia UniversityPennsylvania State UniversityRensselaer Polytechnic InstituteUniv. of California, Los AngelesUniversity of MarylandUniversity of MassachusettsCity University of New York (HBCU)

UniversitiesCarnegie Mellon UniversityColumbia UniversityPennsylvania State UniversityRensselaer Polytechnic InstituteUniv. of California, Los AngelesUniversity of MarylandUniversity of MassachusettsCity University of New York (HBCU)

System IntegratorsLogica CMG

System IntegratorsLogica CMG

Small/Medium EnterpriseRoke Manor Research LimitedSystems Engineering &Assessment Ltd.

Small/Medium EnterpriseRoke Manor Research LimitedSystems Engineering &Assessment Ltd.

UniversitiesCambridge UniversityCranfield University, RMCSImperial CollegeRoyal Holloway CollegeUniversity of AberdeenUniversity of SouthamptonYork University


Government SponsorUS Army Research Laboratories

Government SponsorUS Army Research Laboratories

Government SponsorUK Ministry of Defense

Government SponsorUK Ministry of Defense

US Members UK Members

The alliance creates a joint US/UK team that will match the needs of ITA. IBM’s Watson ResearchCenter at Yorktown Heights in New York is one of the largest IT research facilities in the world. IBM’sHursley Laboratories is the largest IT R&D (research & development) facility in the UK. As a matter offact, IBM is one of the largest employers of Information Sciences professionals (researchers, engineers,and developers) in the US as well as in the UK. Few other companies can claim such a commandingpresence of in-house research capability in both the countries.

IBM’s strength in Information Sciences is augmented by its willingness and ability to workcollaboratively with experts in other companies and technical fields to bring innovative solutions to themarketplace. IBM has research collaboration agreements with several universities and industrialconsortium members, and is involved in a large number of concurrent research projects with several otherconsortium members. We have leveraged on our existing relationships to build a strong consortium for theITA program.


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IBM has a reputation for innovation in technology and services. It has succeeded in maintaining avery good rate for transition of fundamental research into commercial products and services. To reach thisposition, IBM has developed open, collaborative, effective ways of managing and transitioning research.IBM is also a leading business consultancy in the world (after acquiring PricewaterhouseCoopersConsulting in 2002). We will be drawing on that matchless capability to assist ARL and MOD with thetechnology transition process. IBM has its own social sciences centers of excellence; it employsexperienced psychologists to help develop innovative coalition solutions and HCI (Human-ComputerInteraction) products, and these staff will be deployed for ARL and MOD.

2.1. Academic Members of the Alliance

The alliance includes some of the most prominent university professors, widely recognized as titansin their fields of research.

Network Theory: We have teamed with Prof. Jim Kurose and Prof. Don Towsley of UniversityMassachusetts, who are regarded as preeminent network theoreticians in the world. They are joined byProf. Mario Gerla of UCLA, one of the foremost experts in ad-hoc wireless networks, and by Prof. JonCrowcroft of University of Cambridge, who is one of the leading authorities on ad-hoc wireless networksin UK. Other academics of note are Prof. Erol Gelenbe and Prof. Kin Leung of Imperial College, whohave made fundamental contributions to computer network theory and architecture. To initiate work in thearea of biologically inspired computer networks, as recommended in the feedback provided to the originalproposal, we have teamed with Prof. Pietro Lio of Cambridge, who has expertise in bioinformatics andmodeling of molecular biological systems.

Security across a System of Systems: We have joined forces with Prof. Steve Bellovin of ColumbiaUniversity, one of the most influential names in the field of real-world security; Prof. Dawn Song ofCarnegie Mellon University(CMU), one of the preeminent professors in the security area; Prof. JohnMcDermid of York University, one of the leading military security experts in the UK and a member ofUK MoD DSAC (Defence Scientific Advisory Council); Prof. Morris Sloman, the father of the field ofpolicy technologies; and Prof. Kenny Paterson and Prof. Peter Wild at Royal Holloway College, whichhas one of the largest academic security groups in the world.

Sensor Information Processing & Delivery: We are joined by Prof. Debora Estrin and Prof. ManiSrivastava from UCLA, one of the most prominent researchers in the field of sensor informationintegration; and by Prof. Thomas La Porta of Penn State University, one of the leading sensor/wirelessspecialists.

Distributed Coalition Planning and Decision Making: We have teamed with Prof. Nigel Shadbolt ofUniversity of Southampton, a leading contributor to the DIF DTC (UK Data Information Fusion DefenceTechnology Centre) and a member of the AKT (Advanced Knowledge Technologies) consortium; withProf. Derek Sleeman of University of Aberdeen, who has a strong track record in defense research; withProf. Boleslaw Szymanski of Rensselaer Polytechnic Institute, recognized for applications of ArtificialIntelligence techniques to networking; and with Prof. Henning Schulzrinne of Columbia University, thefather of VoIP (Voice over Internet Protocol) technology, who is moving into the decision making area.

Cross Area and Military Expertise: We have also teamed with professors who have a track record ofcross-cutting research, e.g. with Prof. Srinivasan Seshan of CMU, who is a world class expert in thecross-cutting areas of security, sensors, and networks; and with military experts such as Prof. VirgilGligor of the University of Maryland and Prof. Ian Whitworth from the Royal Military College of Scienceat Cranfield University.

HBCU/MI Requirements: City University of New York (CUNY) is our HBCU consortium member.CUNY has one of the preeminent graduate research programs in the US among the historically blackcolleges and universities (HBCU), and the expertise and the technical excellence of CUNY professors isstrong in related technical areas.


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2.2. Industrial Members of Alliance

IBM has teamed with three leading US defense system integrators to bring additional military domainexperience to the alliance. The Boeing Company has a rich heritage of nurturing fundamental research innetwork and information technology leading to the development of technologies that are then integratedinto more mature capabilities for our client base. Specifically, the Mathematics and ComputingTechnology group in Boeing Phantom Works’ Research Organization has been supporting both theCommercial Airplanes and Integrated Defense Systems companies with leap-ahead capabilities inadvanced information technology related to distributed systems and decision-aiding technologies. Thesetechnologies are already a part of large Boeing programs, such as the new Boeing 787 Airplane Programand the Army’s Future Combat Systems Program.

Honeywell is one of the world’s largest aerospace research and technology developmentorganizations. Honeywell’s business spans from sensors to systems, so the technologies addressed inHoneywell’s Advanced Technology organization span across this spectrum as well. Honeywell’sexperience includes internal research and technology development. Honeywell is a significant contractorto the US DoD and primes for contracted research and technology development. The experience includesone of the Cooperative Technology Alliance projects with the US Army Research Laboratory. Honeywellhas product and service facilities across the world and is in the process of executing a broader researchand technology development capability in Europe.

Research and technology development for advanced sensors, in particular wireless capability, andsoftware and hardware associated with network centric operations, are of particular interest to Honeywelland the ITA project. In one particular project, research in wireless networks and sensors is beingconducted for application to land vehicles in Aerospace. Work is also being done in an adjacent market,the Automation Control Solutions industrial applications, which can be leveraged as well. Honeywell is amajor contributor in the area of network centric technology development and application. As an example,Honeywell Advanced Technology organization is a key contributor working with The Boeing Companyon System of Systems Common Operating Environment (SOSCOE) in the Future Combat Systems (FCS)program. The US Army’s FCS is a premier program for DOD on network centric operations. Theseexamples indicate Honeywell’s capabilities and potential contribution to the US Army and UK MOD ITA

program.LogicaCMG is currently leading the Fire

Control Battlefield Information SystemsApplication program in the UK, and bringsexpertise about the needs of UK ArmedForces to the consortium.

The alliance’s capability is furtherstrengthened by the addition of severalsmall/medium enterprises with subject matterexperts in technical areas related to the ITAprogram. Klein Associates, headed by Dr.Gary Klein, is a leader in the field of decisionmaking and coalition planning. KleinAssociates brings psychologists, socialscientists, and the soft skills needed tocomplement Computer Science research toprovide innovative solutions in the field of

decision making and coalition planning. BBN Technologies is an expert in creating military networks,widely recognized for their contributions in the creation of the Internet. Roke Manor Research is theresearch arm of Siemens Corporation and has a long-standing track record of defense communicationsresearch in the UK, more recently focusing in wireless networking. Systems Engineering & Assessment

FundamentalResearch

Bridge from Research to TransitionLight-weight Effective Processes

MilitaryExpertise

UniversitiesCarnegie Mellon UniversityColumbia UniversityPennsylvania State UniversityRensselaer Polytechnic InstituteUniv. of California, Los AngelesUniversity of MarylandUniversity of MassachusettsCity University of New York

UniversitiesCarnegie Mellon UniversityColumbia UniversityPennsylvania State UniversityRensselaer Polytechnic InstituteUniv. of California, Los AngelesUniversity of MarylandUniversity of MassachusettsCity University of New York



IndustryThe Boeing CorporationHoneywell Aerospace ElectronicSystemsBBNT Solutions LLCKlein Associates

IndustryThe Boeing CorporationHoneywell Aerospace ElectronicSystemsBBNT Solutions LLCKlein Associates

IndustryRoke Manor Research LimitedSystems Engineering &

Assessment Ltd.Logica CMG

IndustryRoke Manor Research LimitedSystems Engineering &

Assessment Ltd.Logica CMG



Figure 2

Research Facilitator


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Ltd. (SEA) is a defense contractor with extensive experience in military security issues, and possesses arenowned niche specialty in the area of sensor development and associated technology.

Together, IBM and its industrial partners have a combination of in-house research, military expertise,and pull-through capability required by ITA in US as well as UK.

2.3. Government Members of the Alliance

The researchers of United Kingdom Ministry of Defence and United States Army ResearchLaboratories will be participating in the research and transition programs that are undertaken during thelife-time of ITA. In addition to providing technical and management oversight of the program,Government researchers will also be participating in the various projects that are described in subsequentsections.

2.4. Ways of Working

The alliance members firmly believe that the fundamental research under ITA must challengetraditional assumptions, break down current barriers, depart from compartmentalized thinking, and pursuerevolutionary solutions. In order to develop technology that cuts across traditional vertical boundaries anddelivers the needed fundamental insights, the alliance has developed a horizontal research delivery model.In the first year, twelve cross-cutting research projects have been defined. Each of the projectsdeliberately spans more than one of the four technical areas enumerated above.

Each project team comprises of leading academic, industrial and Government researchers from boththe US and UK, working seamlessly together to develop a new model of international collaboration asdescribed in the accompanying Program Management volume. Moreover, each project team includesfaculty members and industry researchers who have expertise in more than one area. Each project consistsof several activities that focus on specific research problems. The activities conducted within theseprojects may change over the course of this program and projects and activities may be terminated andnew ones defined. But the basic principle of pursuing cross-area, cross-organizational research projectswill remain unchanged.

We have developed the projects in the program plan after an extensive dialogue among all membersof alliance, drawing from our established defense and commercial experience. All the alliance membersmet for a technical workshop in Cambridge UK, in order to develop and refine the contents of the projectsto best meet the needs of US and UK armies.

As part of the project management, the alliance will proactively gather feedback from our transitionprojects in OTA-2 and UKITT as they are deployed, and use this feedback to create a continuousimprovement cycle that will reshape the nature and extent of ITA fundamental research projects andactivities in the future.


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3. Technical Area OverviewThe research program for International Technology Alliance consists of activities in four technical

areas of Network Theory, Security Across a System of Systems; Sensor Information Processing &Delivery and Distributed Coalition Planning & Decision Making. The focus of the alliance is onsynergistic research that spans issues crossing each of the individual technical areas and fosters trans-national collaboration between universities, industries and government organizations.

Thirteen projects will be undertaken in the first year. While each project contributes to more than onetechnical area, each of the first twelve projects to be undertaken is described briefly in the context of thetechnical area to which it makes the primary contributions. The thirteenth project consists of commontasks that span all of the research projects and has been carved by consolidating the common aspects ofseveral projects.

Each technical area has a consortium technical area leader and is supervised by two governmenttechnical area leaders. Each project has a project champion who is coordinating and supervising theactivities within the project.

Each of the technical areas is described in more detail in the following subsections.

3.1. Technical Area 1: Network Theory

Technical Area Lead: Don Towsley, UMass - Amherst

Email: [email protected] Phone: +1 413 545 0207

Project Champions Government Technical Area Leads

Project 1: Don Towsley, UMass – Amherst Anantharam Swami, ARL

Project 2: Kang-won Lee, IBM Tom McCutcheon, DSTL

Project 3: Pietro Lio, Cambridge University

An adaptive self-organizing network that adjusts automatically and rapidly to the ever-changingtactical situations is fundamental to the success of the future vision of the US Army and UK ArmedForces. Wireless communication, a prerequisite for agile operations of coalition forces, is susceptible todetection, identification, location, hostile jamming, abrupt loss of nodes, mobility of elements, andinterference from a variety of sources. Furthermore, the information infrastructure deployed in the fieldmust shield the users from the complexities of the underlying network infrastructure while allowing themto access information required to undertake the mission at hand.

We have defined the following research projects that focus on this area:Project 1 - Theoretical Foundations for Analysis and Design of Wireless and Sensor Networks:

A robust and scalable network infrastructure based on the fundamental understanding on ad hoc wirelessand sensor network will significantly improve the reach of information across coalition members. We willinvestigate the fundamental limits of wireless and sensor networks in the military context to establishtheoretical limits on capacity, scalability, reliability, detection, energy efficiency, and lifetime of networks.In addition, we will develop a mathematical framework within which coalition forces can develop robust,high performance network protocols for military wireless networks.

Project 2 - Interoperability of Wireless Networks and Systems: Insufficient networkinteroperability between coalition nations and even different military units of a nation’s armed forces is acommon barrier that dramatically inhibits the formation of agile mission groups. We will model andanalyze the interoperability of different wireless networks and systems. We will then providePHY/MAC/network/application layer solutions for seamless interoperation, and develop cross-layeradaptation methodologies to achieve optimal performance.



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Project 3 - Biologically Inspired Self-Organization in Networks: Self-configuring and highlyadaptive networks can significantly enhance the survivability of the infrastructure critical to a militaryoperation. Biological systems provide extensive examples of survivable self-organization. We willinvestigate models, theory, and algorithms for creating self-organizing wireless and sensor networksinspired by biological systems.

These three projects are closely linked. The theoretical understanding gained from Project 1 will beused as baselines to evaluate and compare the performance of network protocols and mechanismsdesigned as part of Projects 2 and 3. At the same time, Projects 2 and 3 will supply new requirements andconstraints to be accounted for in the analyses of Project 1. Projects 1 and 3 will also providecomplementary frameworks that Project 2 team members can use to design new protocols such as areneeded for reliable multicast and real-time streaming.

3.2. Technical Area 2: Security Across a System of Systems

Technical Area Lead: John McDermid, University of York

Email: [email protected] Phone: +44 1904 432726


Project 4: Tom Markham, Honeywell Greg Cirincione, ARL

Project 5: Kenny Patterson, Royal Holloway,University of London

Trevor Benjamin, Dstl

Project 6: John McDermid, University of York

Future military coalitions will consist of partners who are heterogeneous in terms of technology,skills, interests, and trustworthiness. These partners will come together in “communities of interest”(CoIs) with common goals, perhaps only for a short period. This imposes new requirements, e.g., theability to negotiate interoperation between groups with different security policies to form a CoI, and theability to make security policy decisions on-line in real-time, not as design-time activities. In general,current security mechanisms will not scale, or be effective, in future systems of systems; the primary aimof this work strand is to “challenge the orthodoxy” and to propose radical new approaches to security thatare appropriate for this new era.

Technical area 2 will address many of these issues directly; others such as emission control(EMCON) and issues of power management act as constraints, rather than being a primary objective. Theunderlying approach adopted by the consortium has three major components:

Explicit use of run-time enforceable policies in security management – A group of systems is onlya true “system of systems” (SoS) if the individual systems have something in common, otherwise it is justa “collection”, not an SoS. Policies (which will need to be hierarchical) provide the common element toenable a set of systems to operate as an SoS. Thus, to form a CoI, there must be agreement on at least aminimum set of security policies;

Security architectures and infrastructures – Secure, flexible CoIs must be composed and beoperational in a resource-constrained environment without the benefits of guaranteed networkconnectivity and centralized security services. The security infrastructure must be sensitive to the powermanagement and EMCON in order to be effectively deployed, and it must take into account the dynamicsof a CoI where members join or leave and members participate in more than one CoI. This imposes newrequirements on the basic security mechanisms for communication and collaboration that need to beaddressed by security architectures and infrastructures;

Risk management – The models of risk and risk management appropriate for a “paper world” are notappropriate in a computer world. To date it has been possible to work round the mismatch in risk models



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between the paper and computer worlds; this is not practical in a network where the membership of CoIsmay change rapidly. Managing risk involves, inter alia, assessing the extent to which individuals,organizations, or their systems are trusted to handle information properly – and also the trust that can beplaced in the information which they provide.

Addressing such issues will lead to a radical change in security policy and mechanisms – but onewhich we think is essential to realize the benefits of NCW and NEC. Without such a radical change we donot believe that the major benefits of NCW or NEC will be delivered. Indeed it is possible thatinappropriate security mechanisms could be a “force divider” so this work is central to managing the risksof developing and deploying NCW and NEC.

To address the above challenges we have defined three research projects:Project 4 – Policy Based Security Management: This project will investigate computing platform

independent policy frameworks to specify and analyze security and networking policies. The end-goal isto provide easy to use mechanisms for refining high-level user-specified goals and decisions into low-level controls such as networking firewalls. The project will develop algorithms to detect policy conflictsand investigate strategies for conflict resolution in CoIs.

Project 5 – Efficient Security Architectures and Infrastructures: This project will develop andanalyze lightweight and adaptive security architectures and infrastructures to facilitate formation of andoperations by secure, flexible CoIs. A focus area in this project would be dynamic trust establishmentamong various members of CoIs by taking into account both positive and negative evidence. In addition,we will explore alternatives to traditional PKIs that are inherently more energy and bandwidth efficientand that promise to provide natural support for coalition operations.

Project 6 – Trust and Risk Management in Dynamic Coalition Environments: In this project, wewill develop a trust and risk management framework that can be used to define and manage the conceptsof trust, risk, and operational benefits in dynamic coalition environments. We will also investigate,develop and validate mechanism for assessing risk and benefit during operation.

A detailed description of the tasks undertaken in these projects is provided in the individual projectsplans described in Sections 7, 8 and 9. During the first year, the focus will be on defining the overallresearch strategy and delivering key initial research results which will form a “platform” for the rest ofthe project. In particular, all three projects have identified a common task of requirement and constraintgathering in the first three months of the program. These requirements and constraints would be gatheredby consulting UK and US Government staff and a review of unclassified literature. A joint meeting ofTA2 team in early November 2006 will be used to bring the whole team to the same understanding ofthese requirements.

In individual project plans, we have identified linkages between different projects (including linkagesto projects in other technical areas) and team members who would explicitly work to explore theselinkages. We will use the November meeting to reinforce these linkages and other collaborationopportunities between different projects.

As a part of forming a “platform” for the remaining duration of the project, additional activities wouldbe undertaken to identify new research tasks that advance the ITA objective of international,collaborative, cross-area research. Team members (including those from other technical areas) wouldcollaborate with each other, advance proposals for the new projects, gather feedback, and strengthen theirproposals in preparation for the BPP.

3.3. Technical Area 3: Sensor Information Processing and Delivery

Technical Area Lead: Thomas La Porta, Pennsylvania State University

Email: [email protected] Phone: +1-814-865-6295


Project 7: Vic Thomas, Honeywell Tien Pham, ARL


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Project 8: Thomas La Porta, Penn State Gavin Pearson, Dstl

Project 9: Boleslaw Szymanski, RPI

Situation awareness superiority can provide tremendous strategic and tactical advantage to coalitionforces over their adversary, especially in cases of asymmetric warfare in urban contexts. Within thecontext of this technical area, we view sensor networks and the data they generate as the powerful toolsthat aid in achieving situation awareness and supporting context-aware decision making and other highlevel military operations.

Wireless sensor networks in military context offer unique deployment, operational and managementchallenges due to their operation in hostile, dynamic, unpredictable, resource-constrained, heterogeneousenvironments which reduce the chances of their long-term predictable operation. Given these operationalrealities, the overarching goal for the proposed research in technical area 3 (or, TA3) is to create thescientific underpinnings for the tools that will compose and deliver the best available, mission criticalintelligence, surveillance and reconnaissance information extracted from a variety of sensory data in atimely, reliable and trustworthy manner to any level of military echelon that needs it, from the frontlinecombat elements in the theater of operation to the highest level of the command and control structure.

To provide the aforementioned information in accordance with the security model to whoever needs itand whenever is needed will require addressing numerous issues each giving raise to a set of researchchallenges. Specifically, providing the needed information will require the collaborative deployment andoperation of sensor networks and data fusion elements that process and deliver data from a largecollection of heterogeneous sensors. The deployment of sensor networks should be content-guided (awarfighter should only need to specify what “high-level” information he or she needs for her task andwhen and not what data should be collected from which sensors) and qualified according to accuracy,reliability, security, urgency, and so on. Here, by deployment we imply not only the physical deploymentand operation of sensor networks but also the logical deployment and operation of the necessary datagathering, processing, and delivery functionalities along an extended network of sensing, computing, andactuating platforms.

Maintaining the quality of the information gathered to support one or more higher-level functions,like decision making, will require the development of the foundations of what “good” information means.This, in turn, will facilitate communicating to the data collecting (sensors) and processing (fusion)elements in a quantifiable manner what kind of information will be sufficient for a task. In turn this willenable sensors and data fusion elements to be deployed and configured in the best possible way to supporta given set of tasks providing information of sufficiently high quality. Furthermore, redeployment andreconfiguration of already deployed sensor networks in the field could happen dynamically in response tocurrent or in anticipation of future operational conditions in order to continue providing, to the extentpossible, the expected information at a sufficient quality as sensor and data fusion elements arecompromised, incapacitated or repurposed.

To research and develop the tools that cope with the above challenges and, thus, reach our goal forTA3, we have “broken” the research required into projects: (i) the characterization of the quality ofinformation extracted from the various sensory sources; (ii) the coordinated and collaborative deploymentof heterogeneous wireless sensors and data processing (or, fusion) elements; and (iii) the on-demandmanagement of the sensor information infrastructure complexity during a mission planning and executionto enable availability of information as required by higher level functions. In summary, the researchprojects in TA3 are:

Project 7 - Quality of Information of Sensor Data: This research project will study formalisms todescribe, analyze and estimate the quality of information delivered by a sensor network. Knowledge ofthe quality of information that is derived from different data sources, expressed through a rich metadataset, which can include representations of the raw sensor data themselves as necessary, will quantifyknowledge (e.g., unreliable, sufficient, superior) and allow decision making entities to appropriatelyweigh information derived from diverse sources and so to make better decisions. This project will help inimproving the richness of information obtained in coalition environments.


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Project 8 - Task-Oriented Deployment of Sensor Data Infrastructures: This research project willinvestigate algorithms, architectures and procedures that will aid in building data source managementsupport taking into account troop and data source mobility across a geographical terrain. Data sources andfusion elements may be proactively deployed and operated to automatically augment the informationgathering experience for improved situational awareness during the execution of a task. Furthermore, thedeployed assets and their operation may be repurposed based on the ever-changing operational context forthe task, continuously engaging and re-engaging the best available assets for the task(s). This project willimprove the operational tempo of a mission, and extend information reach significantly.

Project 9 - Complexity Management of Sensor Data Infrastructures: This research project willdevelop techniques to reduce the complexity of managing sensor data infrastructure, includingtransitioning the operating point of a sensor network in real-time to respond to ever changing and fluidmissions’ goals. It will expose a simplified control interface for managing the multitude of disparatesensing and processing element, finding their optimal operating points and mapping the system trajectoryto reach such points from the current state without disrupting the network operation. Furthermore, it willdevelop data fusion algorithms which include semantic information to reduce the burden of understandingdata sources (sensors) and how they can be effectively be deployed. This research will reducemanagement information overload and will significantly improve sensor network survivability, and willenable improved process synchronization among coalition partners.

To accomplish our goal for TA3 will require the collaborative attainment of the objectives of theresearch tasks in the aforementioned projects. As such, whenever necessary, a research theme in one taskmay appear again in another task that is researching a different element of the theme that is specific tothat task.

There is a strong relationship between the three projects within TA3. Project 7 defines andcharacterizes the “quality of information” that may be gathered from a sensor; this metric is sensor- andphenomena-specific, but not mission specific. Project 9 considers specific missions and thuscharacterizes the value of information. It also allows network operators to easily configure the network tobetter meet mission plans. Project 8 uses the information provides by Projects 8 and 9, and localobservations, to reconfigure the sensor network in a distributed manner. Because local real-timeobservations may differ from mission plans or high-level views, the network may be more reactive tochange than if managed in a purely centralized way.

The interaction between the algorithms of these three projects will lead to the optimal use of networkresources to get the required information to the right places at the right time.

While the PIs in TA3 are assigned to individual projects, it is expected that they will work acrossproject boundaries frequently, will jointly publish papers, and define future research projects together.

The research work in TA3 will be developed in a collaborative manner across the various technicalareas through direct project involvement. La Porta is participating in project 8 collaborating on project 2;Bisdikian and Agrawal from IBM Research are participating in projects 7 and 6; Preece from the U. ofAberdeen is participating in projects 8 and 10, or Nixon and Shadbolt from the U. of Southamptonparticipating in projects 9 and 12. This will further facilitate the research teams in TA3 to maintain tightand persistent connections with research teams across projects in all technical areas that projects in TA3touch upon, or are touched by.

Additional information regarding project relationships, within TA3 and across technical areas,specifically TA1 and TA4, are discussed further in the subsequent project descriptions.

3.4. Technical Area 4: Distributed Coalition Planning and Decision Making

Technical Area Lead: Nigel Shadbolt, University of Southampton




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Project 10: Steven Poltrock, Boeing Mike Strub, ARL

Project 11: Winston Sieck, Klein Assoicates Jitu Patel, Dstl

Project 12: Nigel Shadbolt, Southampton

Coalition operations have emerged as a key feature of military operations in the post-Cold War era.While the U.S. Army and UK militaries have participated in coalition operations throughout theirexistence, it is now the standard mode across the spectrum of military interventions. Virtually all U.S.military operations since 1989 have been conducted as part of a multinational operation. Many of theseoperations have included the UK as a coalition partner.

A coalition operation usually entails an ad hoc arrangement, between two or more “units” or“organizations”, acting together in order to pursue a common objective. They are formed where acommonality of interests exists between nations, be it political, economic or military, allowing coalitionsmembers the benefit of mutual aid in promoting their interest and securing against real or perceivedthreats. Such coalitions are usually created for specific purposes and for a limited duration.

Coalition operations are very different from unilateral operations. They engender a number oforganizational, operational, political, and cultural challenges that are unique to the operationalenvironment, and to the specific composition of the coalition. Organizationally, coalitions can be verycomplex. They often include various branches of the native and foreign military, as well as nonmilitaryorganizations such as non-governmental organizations (NGOs) and private voluntary organizations(PVOs), each bringing their own unique cognitive and collaborative styles (‘culture’), doctrinallanguages, technological capabilities and core competencies. This blending of capabilities makes possiblecertain operations that a single coalition member could not or would not conduct unilaterally.

Operationally, coalitions span the entire range of military operations from combat to humanitarian tostability and support operations (SASO), identified in the 1990’s by US Marine Corps General Charles C.Krulak, as the “Three Block War”. The precise role of armies in these operations varies according to eachpolitical and military situation. In some situations military operations are subordinate to political anddiplomatic efforts. In such circumstances, coalitions have to be very creative in finding ways to deliverappropriate and effective levels of military leverage that support political-diplomatic initiatives within theprescribed limitations.

The whole issue of blended teams, agile mission groups, mutual understanding, trust andpredictability between team elements becomes a crucial concern. And in such complex situationsredirecting the efforts of teams and enabling adaptivity of coalition elements is vital.

Concurrent execution of peace-keeping, humanitarian and anti-insurgent operations requiresbalancing multiple issues, such as political imperatives, cultural sensitivities, and military objectives.Military planners and commanders need planning and support tools to understand and analyze situationsquickly to make timely and effective decisions. In the absence of suitable cognitive informationmanagement technologies, military planners and commanders will be overwhelmed with the amount ofinformation available in the networked battle-space. Current coalition operations are difficult enough, butthe added bandwidth and corresponding information flood anticipated by full network centric warfare willfurther stress the ability of coalition partners to 'Orient, Observe, Decide, and Act' in a cohesive manner.

To be effective in future operations, distributed coalition teams will require extended informationenvironments with planning and decision support tools that can adapt to different contexts andrequirements including cultural and organizational differences between coalition members.

We have organised our research proposal in TA4 around three substantive projects that we outlinebelow. We have also identified a number of horizontal coordination activities across all three projectswhich we also describe.

The three core projects in this theme are all united by a common requirement to understand andsupport the complex human, social and technical interactions anticipated in future coalition operations.Tools and techniques will be researched to capture the varieties of social interaction that comprisecommunication and collaboration, miscommunication and conflict. Our aim is to evolve a next generation


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of military Information Management (IM) and Information Exploitation (IX) capabilities. The SemanticBattle-space Infosphere (SBI) refers to an extension of the conventional Joint Battle-space Infosphere(JBI) in which information is integrated and interpreted with respect to a rich common semantic frame(s)of reference. With an understanding of team interaction, command models and an informationmanagement framework, it will be possible to characterise and build tools and methods to enhanceDCPDM.

The core projects are:Project 10 - Mission Adaptive Collaborations: The essence of this project is to develop tools,

methods and techniques to analyse and synthesise coalitions of agents (human and synthetic), to discoverhow these teams can understand one another and how they can most effectively adapt and redirectthemselves. This project will exploit insights from project 11 as well as requirements from project 12.

Project 11 - Command Process Transformation and Analysis: This project focuses on developingan understanding of the command processes within coalitions as well as the processes at workdetermining their external interactions. It will analyse the cognitive and socio-cultural factors thatfacilitate or impair communication and understanding. The aim is to provide the means to monitor and, ifnecessary, transform these processes. The work will inform and be informed by projects 10 and 12.

Project 12 - Shared Situation Awareness and the SBI: This project will research tools and methodsfor understanding situations unfolding in a distributed environment. The effort to enhance situationawareness will also integrate a range of planning and decision making services. The overall approach willbe sensitive to the outputs of projects 10 and 11.

The research effort for Technical Area 4 (DCPDM) contains two cross-cutting initiatives that wereoutlined as part of the Fundamental Research Volume (FRV). These initiatives form the backdrop to muchof the work undertaken throughout Projects 10, 11 and 12, and they are also relevant to other projectswithin the ITA. Another cross-cutting initiative, the creation of an ITA scenario is included as part ofproject 13.

Ontology Engineering: this initiative will aim to provide a generic suite of ontologies for use by allprojects within the ITA. Ontology engineering features as a specific sub-task of Project 12, but theaim for the cross-cutting initiative is considerably broader than that proposed in Project 12. The aim isto provide an ontological infrastructure to support information and knowledge processing throughoutthe ITA initiative (as opposed to specifically supporting planning and decision-making in the contextof Project 12). The initiative will involve the use of ontology engineering techniques to provide asuite of ontologies for use throughout the ITA. In addition, a semantic portal will need to be providedto enable browsing and exploitation of these ontologies.

Human Aspects: this initiative aims to understand and appreciate the socio-cultural and socio-technical issues associated with coalition planning and interaction. The aim is to provide a frameworkfor dealing with the psychological and cultural barriers to communication and collaboration incoalition contexts. A workshop is planned for the 11-13th September 2006 in which the variousstrategies for dealing with these human science issues will be resolved. The principal effort willderive from Projects 11 and 12 with Boeing and IBM helping coordinate.

3.5. Cross Area Project: Project 13

As the program plan for the 12 projects described above were developed, it became apparent thatseveral activities needed to be conducted in common across several projects. In order to eliminateredundant efforts, we have collected all such activities into a common project. Project 13, thus contains ofa set of tasks that play a supporting role to the technical activities that are underway. Project 13 will havecontributions from all of the team members of the alliance, and will provide common tasks such as thedevelopment of a ITA specific scenario and the collection of data that enables much of the research work.


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3.6. Project to Technical Area Mapping

While all of the projects are designed to span more than one technical areas, each project has a closeraffinity to one of the technical areas. The mapping between the primary technical area to which a projectcontributes to are shown graphically in the figure below.

Mission Adaptive System of Systems

Shared Situational Awareness and SemanticBattlespace Infosphere.

Command Process Transformation and Analysis

Complexity Management of DataInfrastructure

Task-Oriented Deployment of Data Sources

Quality of Information of Sensor Data

Trust and Risk Management in DynamicCoalition Environments

Security Mechanisms in Dynamic CoalitionEnvironments

Policy-Based Security Management

Biologically Inspirer Self-Organizing Networks

Information Management in aNetwork of Networks

Theoretical Foundations for Analysis andDesign of Wireless and Sensor Networks

11

22

33

44

55

66

77

88

99

1010

1111

1212

TA1:Network Theory

TA2:Security Across ASystem of Systems

TA3:Sensor Information

Processing & Delivery

TA4:Distributed Coalition

Planning and DecisionMaking

Cross-Area Activities.1313

The following thirteen sections describe the five year vision and the first year plan for each of theresearch projects in more detail.


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4. Project 1: Theoretical Foundations for Analysis and Design ofWireless and Sensor Networks

Project Champion: Don Towsley, UMass – Amherst


Primary Research Staff Collaborators

Jim Kurose, Umass-Amherst Srinivasan Seshan, CMU

Don Towsley, Umass-Amherst Jon Crowcroft, Cambridge University

Kin Leung, Imperial College Anantharam Swami, ARL

Arup Acharya, IBM Stuart Hendren, DSTL

Prithwish Basu, BBN

Jason Redi, BBN

Bilal Khan, CUNY

Zhanyang Zhang, CUNY

Emma Jones, SEA

4.1. Project Summary/Research Issues Addressed

The goal of this research project is to develop theoretical foundations for deriving fundamental limitson the performance and reliability of energy-constrained mobile wireless and sensor networks and fordesigning robust network protocols and solutions that account for constraints and requirements introducedby battlefield scenarios in coalition settings. These constraints and requirements include mobile andhighly dynamic users, communication patterns imposed by military operations, the need to remainundetected, unreliable and disappearing sensors and resources, heterogeneity in resource capabilities andprotocols, uncertain, partial, and/or erroneous information about network state, severe energy constraints,and high reliability requirements. A well-designed operational network ensures the communications andmovement of data required for a military force to maintain situational awareness while adapting as thesituation changes. A fundamental understanding of network behavior is essential to create and maintainsuch a network.

There has been substantial progress in understanding of wireless networks in the context of single-hop cellular networks as well as multi-hop ad hoc and sensor networks. Starting with the pioneering workof Gupta and Kumar fundamental limits on capacity and connectivity of wireless ad hoc networks andcoverage of sensor networks have been established as well as tradeoffs between network capacity andend-to-end delay. These results provide relevant benchmarks and design guidelines for wireless andsensor practitioners to follow. However, the results have been developed simple homogeneous networkswithout much consideration of MAC, physical layer, network protocols, and the many constraints andrequirements typical of a hostile battlefield scenario (frequent node failures, intermittent links, MIMO,energy constraints, accessibility to malicious users). Another shortcoming of the previous work inwireless networks has been its focus on a single resource, namely communication bandwidth. Current andfuture wireless networks are characterized by nodes that include numerous additional resources such asprocessing, storage, and multi-modal sensors; all of these coupled with power constraints. The

4.2. Technical Approach

Our approach is two pronged: (1) to obtain limits on communications/sensing capacity, probability ofdetection, coverage, lifetime and mission-specific metrics using a combination of information theory,


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game-theory, and stochastic models, and (2) to resource allocation algorithms that optimize user orientedperformance metrics using ideas from robust optimization theory and from distributed utility-basedoptimization. During the first year we will focus on various aspects of military wireless networks in asomewhat independent manner. On of the first year goals of the project are to develop models andformulations focusing on combinations of these various aspects. Three of the tasks will focus onperformance limits that account for (1) advanced antenna technologies such as MIMO, (2) energyconstraints, (3) cooperation among nodes. The fourth task will focus on robust resource allocation andoptimization

Task 1: Performance and Design of Wireless Ad-Hoc Networks Using MIMO AntennaResearchers: K. Leung (Imperial College); A. Acharya (IBM); E. Pritchard (SEA); J. Kurose and D.

Towsley (UMass)

The multiple-input-multiple output (MIMO) antenna can provide spatial diversity and multiplexinggain in wireless networks. It also represents a powerful technique for interference mitigation andreduction in interference-limited environments. Therefore, to meet the needs of future tacticalcommunications, it is advantageous to equip the associated wireless ad-hoc networks with MIMO antennacapabilities.

MIMO as a physical-layer technique impacts considerably the design of upper-layer protocols,including medium access control (MAC), packet scheduling, power control, routing, transport protocol,and ultimately the overall quality of service in a wireless network. To illustrate this, consider packetscheduling for a form of MIMO system with beam forming. By partitioning multiple antenna elementsinto several groups, beams can be formed and used to deliver data simultaneously to multiple destinationnodes/users, perhaps at different directions. Thus, with appropriate scheduling algorithms, MIMO offersadditional flexibility in avoiding excessive interference and ability to exploit multi-user diversity, whilemaintaining fairness. Control mechanisms to dynamically adjust transmission power of each individualbeam can be used to further improve system performance. Using such integrated algorithms for radio-resource allocation, th link capacity between any given node pair is not fixed as in a wired network, andcan vary greatly in time, thus causing a significant impact on routing and transport-layer performance.Clearly the optimal system design can only be achieved by jointly considering these inter-related controlmechanisms and protocols together.

Although much work related to MIMO has been carried out since the mid 90’s, it is not adequate forthe following reasons. First, to the best of our knowledge, existing work typically considers a single basestation (one transmitter) serving multiple users. Mutual interference, inter-dependency and dynamicsamong multiple transmitting nodes in ad-hoc networks using MIMO have not been considered in theprotocol/control design from a cross-layer perspective. Second, how the MIMO and its associatedprotocols in the multi-transmitter scenarios affect transport-level performance is not well understood.Although it appears that the sum capacity is not increased with the introduction of MIMO, it is less clearwhat the effect is on end-to-end delays and capacity region. Evidently, devising the optimal system andprotocol design represents a very complicated problem. We propose the following approach.

As a first step, we plan to devise and study the performance of the “best” integrated algorithms for aselected subset of these control mechanisms (e.g., joint packet scheduling and MAC protocol) byconsidering the properties of the integrated algorithms and engineering insights. Reference [1] representsour first attempt in this direction for a one-hop scenario. We plan to extend the approach to multi-hopscenarios and multiple source-destination pairs (i.e., multiple transmitters). For simplicity, we will beginwith the two-hop relay network. We will also study the performance impacts of imperfect channel statusinformation in the network. As another approach to understanding performance impacts of MIMOsystems, we will also consider ad-hoc relay networks because they can achieve similar diversity gain asMIMO systems do. For a relay network using a strategy such as amplify-and-forward and decode-and-forward [2] to provide extra data paths for cooperative diversity, we study the effectiveness of cooperativediversity from the perspective of MAC/link protocols. Our approach will be to analyze the protocol


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performance by including an abstract, perhaps simplified representation of the MAC or link-leveloperations.

The main objective of this step is, not to develop another new joint algorithm, but, instead, to gaininsights into the highly inter-related issues and how the joint algorithms affect system performance inmulti-transmitter scenarios. Based on this understanding, we will then construct analytical models tostudy the fundamental performance limits of wireless ad-hoc networks using MIMO antenna withpractical feedback of channel information. Our plan is to use these models to shed further new insightsinto how best these integrated protocols should be refined for optimal performance. We believe thatcarrying out the protocol design/refinement and performance modeling iteratively will lead us to muchbetter, fundamental understanding of the optimal design and performance of wireless ad-hoc networksusing MIMO antennas.

Task 2: Energy consumption, maximum stable throughput or transport capacity, and sensorcoverage

Researchers: P. Basu, J. Redi, BBN; Z. Zhang (CUNY); D. Towsley, Umass ; K. Leung, (ImperialCollege)

We consider a wireless ad hoc and/or sensor network in which nodes are duty cycling theirtransceivers to conserve energy. However while energy conservation is an important goal in many suchtactical networks, this can come at the cost of low throughput capacity and poor sensor coverage. Hence itis important to study the fundamental tradeoffs between capacity, energy conservation, latency, and sensorcoverage in such duty cycling networks. We describe two related subtasks, the first one focusing onenergy conservation and capacity in a wireless ad hoc network and the second on energy conservation andsensor coverage in a wireless sensor network.

Energy and capacity. In our system model, time is divided into slots. In each time slot, every node’stransceiver is either transmitting (ON_TX), receiving (ON_RX), idle, or off to conserve precious batteryenergy. Since the optimal time-division multiple access (TDMA) schedule is often difficult to compute,researchers have proposed coordination by pseudo-random duty-cycling [2,3]. Each node’s ON/OFFschedule is random in nature but by exchanging a small set of parameter values (such as seed and cycleposition of pseudo-RNG) each node can construct the exact ON/OFF state information of all of itsneighbors in subsequent slots. In [4], the authors propose to reduce the amount of wasted transmissionenergy by transmitting to a neighboring node only if the node is ON and receiving in a slot. In [5], theauthors propose exchanging the schedule among all two-hop neighbors with transmitters competing for aneighboring receiver’s slot in a process similar to slotted-ALOHA. Note that in theory such informationcan be shared network wide for better capacity and energy gains (as in TDMA).

We analyzed the tradeoffs between energy consumption and delivery capacity of six duty cyclingschemes (two of them ours) in previous research funded by the CTA program [3]. We assumed that allnodes in the network have fixed transmission, reception and off probabilities in each slot that add up tounity. However, in that analysis, our definition of capacity is a restrictive one: “hop delivery capacity” isdefined as the maximum number of successful transmissions to intended recipients among neighbor nodesfor a given duty cycling ad hoc/sensor network. In the proposed research, we will incorporate an end-to-end capacity metric into the duty cycling framework. The throughput capacity between nodes X and Y isdefined as the number of successful receptions of flows (or total number of bits) initiated by X andintended for Y in a given period of time T. Such a flow will typically involve a chain of transmissionsfollowed by successful receptions and subsequent transmissions downstream. In ITA Year 1, we proposeto address the effect of multi-hop routing, queuing and duty cycling on the maximum stable throughputcapacity of the network which is defined as the maximum tnumber of bits that can be injected by allsources into the network at a certain rate of energy consumption in steady state without overflowing ofqueues while offering bounded latency. This definition of capacity is similar to the one used in [6] but isaugmented with an additional dimension of energy conservation.

To investigate the fundamental limit of maximum stable throughput capacity of a duty cyclingnetwork with bounded energy, we propose two optimization problems with perfect knowledge of network


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state1. In the first problem, the traffic matrix (source, destination and load of each flow) is known over acertain period of time T, and the problem is to assign duty-cycling (ON/OFF) probabilities to individualnodes in the network such that the energy expenditure for the entire network over time period T isminimized. The second problem is: given energy budget E, assign ON/OFF probabilities and a datatransmission schedule (both over a period T) so that the resultant energy expenditure is less than E and thenumber of bits received successfully at intended recipients is maximized. Both problems are most likelyto be NP complete but by asymptotic analysis we are hopeful that we can establish good bounds.Researchers have shown that the maximum stable throughput capacity problem in other types of ad hocnetworks can be modeled as a multicommodity flow problem and hence it becomes amenable to max-flow/min-cut based analysis [18]. Although our problem is significantly more challenging because of theduty cycling and energy dimension, we believe that max-flow/min-cut approaches will prove to be usefulfor the capacity analysis.We shall first attempt to solve this problem for simple networks such as line,grid, or circular ad hoc/sensor networks before we attempt to solve it for networks with randomtopologies.

Looking towards years 2 and 3, we will relax some of our assumptions and ask questions such aswhat is the effect of imperfect synchronization and imperfect channel state information CSI? In addition,we will also determine in what sense our pseudo-RNG approach is optimal. We will also study the caseswhere the traffic matrix is not deterministic but statistically known, and when the queueing discipline isnot just first-come-first-served but also includes priority scheduling.

Energy and coverage. Suppose that we want to design and deploy a sensor network with anoperation lifetime of 90 days, given a coverage requirement, and energy consumption rate. We need toanswer questions such as, what is the minimum sensor density necessary and the necessary duty-cyclefrequency to achieve the coverage. One way of doing so is through the use of clusters. A Sensor ClusterLocation Algorithm (SCLA) is used to elect a cluster head. Other nodes become members of the cluster.The cluster usually contains more members (redundant sensors) than what is needed for requiredconnectivity and coverage. A Sensor Cluster Maintenance Algorithm (SCMA) can be used to sustainsensor cluster function by replacing failed sensors with redundant ones to increase robustness andreliability. Energy efficiency and conservation is achieved by duty-cycling among cluster members. Ineach cluster, some members stay active to provide connectivity and coverage while other members are“asleep”, turning off sensing and communication function, to conserve energy. Sleeping members wakeup periodically and replace failed nodes. We will explore the tradeoffs between coverage and energyefficiency for generic duty cycling algorithms. We will also explore the tradeoff between sensor networklifetime and a variety of parameters such as sensor density, battery power, energy consumption rate, nodefailure rate and duty-cycle frequency.

Task 3: Fundamental Limits of Cooperative NetworksResearchers: J. Kurose, D. Towsley, Umass ; P. Basu, J. Redi (BBN); B. Khan (CUNY); K. Leung

(Imperial College)

In this task, we will study a number of different ways that wireless users can cooperate with eachother to provide better performance. For each of these we will develop models that can be used to obtainupper bounds on performance. In particular, we will consider three different forms of cooperation. Theseinclude:

Networks within which nodes cooperate to send and receive a single data stream.

1 This means all nodes are aware of the entire network topology and the full source-destination traffic matrix, ifapplicable (e.g. in the first problem). Since we want to establish limits on stable transport capacity, assuming suchan omniscient view is not unreasonable. The protocol overhead of gathering this omniscient view of the networkstate is important but will be a subject of future investigation.


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Networks within which nodes cooperatively combine and decode packets from different data streams.This is exemplified by network coding.

Networks with infrastructure where a subset of nodes are available solely for the purpose of aidingother nodes to transfer data to each other.

Networks within which nodes cooperate in moving themselves to positions where they may betterroute traffic.

We have partial results for some of these problems. In the case of cooperative transmission, we haveshown through simulation [9] that there is percolation in the case of a 2D network. By this we mean thatthere is a node density threshold below which the network is not connected if the node density lies belowthe threshold and is connected (almost surely) if it lies above the threshold. Our simulations suggestscenarios where the percolation threshold may be zero. We would like to prove this. For example, in thecase of a 1-D network without cooperation, there is no percolation. However, our simulation resultssuggest that percolation occurs when nodes cooperate in a wide range of scenarios. One of our goals isto formalize and prove this.

In the case of network coding (NC), we have shown that it does not change the asymptotic sumcapacity of a wireless network [10]. However, network coding does provide some improvement [11]. Infact, [11], which is based on a simulation and measurement study, suggests a significant improvement incapacity due to network coding. Our work [10] suggests that this may not true. We conjecture that muchof the improvement seen in [11] is due to the use of a poor performing wireless MAC (802.11) rather thaninherent advantages of NC. One goal of this task is to determine a bound on the performanceimprovement that network coding can provide in a wireless network. Another is to determine how thedynamics of a military wireless network affects the performance of NC. We will focus on several metricsincluding the sum capacity, the capacity region, end-to-end latency, and energy consumption.

In the case of a network with infrastructure, we have identified an achievable throughput when allusers are required to use the same transmission power [8]. However there is a gap between the achievablethroughput derived in [8] and a capacity bound for such a network. A goal of this task is to consider asystem where users are not required to transmit at the same power. We hypothesize the existence in thiscase of a system design that will asymptotically achieve a throughput that matches the capacity. As partof this task, we will also identify the benefits that arise from introducing infrastructure and, guidelinesregarding the number of infrastructure nodes that need to be introduced in order to achieve a significantperformance improvement. As before, our focus will be on the sum capacity, the capacity region, end-to-end latency, and energy consumption.

In the case of cooperating mobile routers, we will identify the best possible performance that can beachieved by placing them optimally. We will then develop scalable distributed algorithms that canachieve a robust placement of these routers, i.e., one that works well when there is uncertainty in thelocation of other users. We expect interacting with the Task 4 researchers on this. Throughout, the focuswill be on minimizing bit error rates (BER) for existing connections. This has the effect of maximizingsum capacity. As time permits we will look at other metrics.

Task 4: Robust Routing in Multihop Wireless NetworksResearchers: J. Kurose, D. Towsley (Umass); B. Khan (CUNY); K. Leung (Imperial College)

The goal of our research in this project is to gain fundamental insight into the design of a robust set ofroutes in multihop wireless networks. Informally, a set of routes is robust if it performs well under a widevariety of operating circumstances (e.g., changes in exogenous traffic arrival rates, or changes in networktopology/link-status due to mobility and time-varying link-characteristics), with little or no additionalcontrol overhead as traffic or topology changes. While a robust set of routes will not provide optimalperformance over all scenarios (traffic matrices, topologies), it should provide “good” performance over awide range of scenarios. Our goal is to characterize this tradeoff between a robust set of routes and thecase in which routes are optimized individually for each particular scenario. While there is a cost to


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achieving robustness, there is also a gain – the robust solution will require significantly less overhead(and arguably less complexity) than a routing protocol that must monitor, detect changes, andcontinuously reconfigure.

Our previous work on robust routing [15,16] has considered the problem of robust route optimizationin wireline networks, where the goal was to find a robust set of routes that provide “good” performanceover a wide range of traffic matrices (TMs). A traffic matrix specifies the mean flow rate between source-destination pairs; the variability in the traffic arrival rate around this mean is reflected in the calculation ofthe per-link costs (e.g., delay). We have considered two performance metrics: average and worst caseperformance over the set of TMs. Our initial research has shown that the problem of finding a set ofroutes that optimizes a linear combination of average and worst case performance over the TMs can besolved using a distributed gradient-based approach. We have shown, using operational data from a Tier-1network, that one can significantly improve the worst case performance, while only marginally decreasingaverage case performance.

In our proposed work under this ITA, we will begin by considering the problem of finding a set ofroutes that performs well in the face of single-link failures, with minimal route reconfiguration whenfailure occurs. Our performance metric of interest is the sum of all link costs in the network, where eachindividual link cost is a convex, increasing function of link data rate [13,15,16]; the set of routes used isrepresented by the flows {fG

s,d(i,j)}, for all s,d,i,j, in a baseline (no-failure) network topology G, wherefG

s,d(i,j) is the flow rate from source s to destination d routed over link i,j. As in [15], we will consider thetwo cases of minimizing average network-wide cost, as well as the problem of minimizing the maximumsingle link cost.

We will begin by considering a simple local rerouting policy, DEST [12], in which node i reroutesflow fG

s,d(i,j) over other incident outgoing links, {(i,k) k != j} when link (i,j) fails. This re-routing is a localdecision, and hence can be accomplished with low overhead. Given DEST, the initial set of routes in Gmust be chosen so that there exists a feasible set of routes that can carry the re-routed traffic in all linkfailure scenarios. Thus, the performance price to be paid for the simplicity of DEST is two-fold: not onlymay there be an alternate re-routing of flows with superior performance (but requiring non-local actionsto re-route the flows), but the initial set of routes must be chosen to allow for local re-routing uponfailure. This baseline set of routes will likely not be optimal for the baseline (no failure) case. We willcompare DEST with a COMPLETE policy that globally re-computes the optimal set of routes when link(i,j) fails. Because COMPLETE requires global re-computations, it will incur significant overhead, butwill result in optimal performance in the baseline scenario and in each individual failure scenario; DESTon the other hand will result in non-optimal performance but has the advantage of considerably lessoperational complexity that COMPLETE. It is this tradeoff between performance and overhead that weseek to understand

The questions we will address include the following: Given a distribution of link failure probabilities, and assuming that only one link fails at a time,

what is the performance and overhead differences between DEST and COMPLETE? We considerboth the convex link cost metric discussed above, as well as the capacity needed to ensure that aDEST re-routing policy exists [14].

The discussion above has not explicitly considered the wireless nature of the network, and so ourvery first task will be to formulate these problems in the wireless domain. The first complication isthat the resources that determine marginal performance gains are not only downstream linkbandwidth towards a destination, but node-local resources as well (e.g., TDMA slots). Our recentwork [17] handles this coupling of resources at sending/receiving nodes, but theinterference/coupling with nodes other than the receiver must still be considered. We will considerwireless channel access protocols that includes channelization approaches such as TDMA (whichlink to the research described above in Task 2), as well as non-channelized protocols such as802.11.

What is the complexity of the DEST routing policy, and is there a distributed algorithm that can beused to compute the optimal set of routes under DEST?


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Additional re-routing policies can also be considered: (i) a “by-pass” strategy that re-routesindirectly from i to j over an alternate path, and (ii) globally re-routing only those sessions that hadbeen routed over the failed link, leaving all other session routes unchanged.

4.3. Relevance to US/UK Military Visions

The research conducted as part of this project will provide insight for the development of betternetworks for the battlefield (capacity, robustness, latency, energy efficiency). This will result in improvednetwork design, configuration, and control. An important part of will be the focus on military appropriatemetrics. Finally, the theory developed through this project will facilitate the deployment and planning ofwireless networks in the battlefield.

4.4. Collaborations and Staff Rotations

There is scope for significant collaboration both with the other two projects within Technical Area 1and the other three technical areas. As an example of collaboration within TA-1, several of our tasks willbenefit from work conducted in Project 3. Several of the tasks require distributed algorithms fororganizing the network. These will come from Project 3. At the same time, several tasks in Project 3 canbenefit from the development of models exploring the performance limits of different strategies for self-organization. Similar ties exist to Project 2.

We will collaborate with projects outside of TA-1. In particular, during the first year we will exploreissues in common with projects in TA-3. For example, the theories developed as part of our research mayshed light on tradeoffs between resource usage in a sensor network and the quality of the results that areobtained.

Collaboration will be facilitated through two face2face project meetings, one of which is expected toinclude members of the other two projects. In addition, several of us are collaborators within otherprojects; we will attend all scheduled events associated with those projects. Last, we will scheduleexchanges of graduate students as well as principal personnel within this project.

4.5. Relation to DoD/MoD and Industry Research

Two of our project members (Basu, Redi) are members of an existing CTA. Their proposed work onenergy efficiency will build on earlier work conducted as part of that CTA. None of the other proposedwork overlaps with existing DoD/MoD and industry research. Although some efforts are under wayexploring issues such as MIMO, they focus on the design of specific systems and algorithms rather thanfocusing on fundamental performance limits.

4.6. Research Milestones

Research Milestones

Due Task Description

Q1 Task 1 Enhance, quantify performance of initial joint physical-layer, MACand routing algorithms proposed in [1] for wireless ad-hoc, relaynetworks. (IC, SEA)

Q1 Task 2 Detailed modeling and formulation of both optimization problems: (1)max capacity given limited energy and (2) min energy given trafficmatrix (BBN, Umass, Imperial College)

Q1 Task 4 Problem definition in wireless domain, performance metrics andtradeoffs identified (UMass)

Q2 Task 1 Extend and investigate the joint algorithms for wireless ad-hoc


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networks using MIMO antenna. (IC, SEA, UMass)

Q2 Task 2 Development of solution approaches to optimization problemsmentioned earlier for simple networks such as line and grid (BBN,Umass)

Q2 Task 3 Quantify potential benefits of NC in a static wireless network (Umass)

Q2 Task 3 Determine (near) optimal placement of cooperative routers thatminimizes bit error rate (BER) (in absence of mobility budgetconstraints). (CUNY, Umass)

Q2 Task 4 Complexity analysis. Centralized solution approaches (CUNY, UMass)

Q3 Task 1 Identify the basic characteristics and features of the optimal jointcontrol algorithms for wireless ad-hoc networks.(IC, SEA, UMass)

Q3 Task 2 Simulation study to establish validity of analytical solutions (BBN)

Q3 Task 3 Derive capacity of wireless networks with infrastructure support(UMass, IBM)

Q4 Task 1 Based on the basic characteristics, develop initial analytic models tounderstand performance limits of wireless ad-hoc networks usingMIMO and relay. (IC, UMass)

Q4 Task 2 Development of solution approaches to optimization problems forrandomly deployed ad hoc and sensor networks and establish bounds(BBN, UMass)

Q4 Task 3 Develop distributed (near) placement of cooperative routers thatminimizes bit error rate (BER). (CUNY)

Q4 Task 3 Derive connectivity properties of distributed MIMO networks (UMass,IBM)

Q4 Task 4 Distributed solutions, paper submission, e.g., to infocom or icnp CUNY,UMass)

4.7. References

1. C. Akcaba, R.U. Nabar, K.K. Leung, “A PHY/MAC Approach to Wireless Routing,” IEEE ICC2006, Istanbul, Turkey, June 11-15, 2006.

2. J. N. Laneman, D. N. C. Tse, G. W. Wornell, “Cooperative Diversity in Wireless Networks:Efficient Protocols and Outage Behavior,” IEEE Trans. On Information Theory, Vol. 50, No. 12,Dec. 2004, pp. 3062-3080.

3. L. Dai and P. Basu, “Energy and Delivery Capacity of Wireless Sensor Networks with RandomDuty-Cycles,” Proc. IEEE ICC 2006, Istanbul, Turkey, June 2006.

4. J. Redi, I. Castineyra, C. Partridge, K. Manning, R. Rosales-Hain, R. Ramanathan, and S. Kolek,“Joint Architecture Vision for Low Energy Networking (JAVeLEN) – DARPA-ATOConnectionless Networking Program, Phase I,” BBN Technical Report 8408, Dec. 2004.

5. R. Rozovsky and P. R. Kumar, “SEEDEX: A MAC protocol for ad hoc networks,” ACMSymposium on Mobile Ad Hoc Networking & Computing (MobiHoc), Oct. 2001.


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6. M. Neely and E. Modiano, “Capacity and Delay Tradeoffs for Ad-Hoc Mobile Networks,” IEEETrans. Info Theory, June 2005.

7. Z. Zhang, W. Gavin, “A Cluster Based Feedback Model for Sensor Deployment Managementand Coverage Verification in Wireless Sensor Network Applications”, the 2005 annualInternational conference on Wireless Networks”, Las Vegas, June, 2005, pp. 441-447.

8. B. Liu, Z. Liu, D. Towsley. “On the capacity of hybrid wireless networks”, Proceedings ofINFOCOM 2003.

9. S. Song, D. Goeckel, D. Towsley. “Collaboration improves the connectivity of wirelessnetworks”, Proceedings INFOCOM 2006, April 2006.

10. J. Liu, D. Towsley, D. Goeckel.. “The throughput order of ad hoc networks employing networkcoding and broadcasting”, submitted to IEEE MILCOM 2006.

11. S. Katti, D. Katabi, W. Hu, H. Rahul, M. Medard, “The Importance of Being Opportunistic:Practical Network Coding For Wireless Environments,” Proceedings of Allerton, 2005.

12. J. Anderson, B.T.Doshi, S. Dravida, and P. Harshavardhana, “Fast restoration of ATM networks,IEEE Journal on Selected Areas in Communications, vol. 12, no. 1, pp. 128–138, 1994.

13. R. Gallager, “A Minimum Delay Routing Algorithm Using Distributed Computation”, IEEETransactions on Communications, Vol. 25, No. 1, pp. 73-85.

14. K.Murukami and H.Kim, “Optimal capacity and flow assignment for self-healing atm networksbased on line and end-to-end restoration,” IEEE/ACM Transactions on Networking, vol. 6, no. 2,pp. 207–221, 1998.

15. C. Zhang, Z. Ge, J. Kurose, Y. Liu, D. Towsley, “Optimal Routing with Multiple TrafficMatrices: Tradeoff between Average Case and Worst Case Performance,” Proc. IEEE Int. Conf.Network Protocols, 2005.

16. C. Zhang, Y. Liu, W. Gong, J. Kurose, R. Moll, D. Towsley, “On Optimal Routing with MultipleTraffic Matrices, “ IEEE Infocom 2005.

17. C. Zhang, J. Kurose, Y. Liu, D. Towsley, M. Zink, “A Distributed Algorithm for Joint Sensingand Routing in Wireless Networks with Non-Steerable Directional Antennas,” to appear in Proc.IEEE Int. Conf. Network Protocols, 2006.

18. C. Peraki and S. Servetto, “On the Maximum Stable Throughput Problem in Random Networkswith Directional Antennas”, Proc. ACM MobiHoc 2003.

http://nms.lcs.mit.edu/~dina/pub/cope.ps

http://nms.lcs.mit.edu/~dina/pub/cope.ps


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5. Project 2: Interoperability of Wireless Networks and SystemsProject Champion: Kang-Won Lee, IBM



Jason Redi, Prithwish Basu, BBN Kin Leung, Imperial College

Mario Gerla, UCLA Tom Markham, Honeywell

Jon Crowcroft, Cambridge University Tom La Porta, Penn State

Srivatsan Varadaranjan, Honeywell Brian Rivera, ARL

Robert Hancock, Roke Manor Research Stuart Farquar, DSTL

Kang-Won Lee, IBM Research

Nancy Griffeth, CUNY


One of the key requirements of coalition networks is to support efficient information sharing acrossheterogeneous wireless networks in a variety of tactical scenarios for US and UK armed forces. In thiscontext, the network should allow seamless interconnection among all participants in coalition forcesenabling effective data dissemination and retrieval of mission-critical information with ease. To achievethis goal, we join forces in this project to gain fundamental understanding on the interoperation ofheterogeneous wireless networks. We will start this effort by building mathematical frameworks andmodels of interoperation, and derive fundamental bounds in performance, efficiency, overhead, andoptimizability. Then these analytical results can be validated using simulations and measurement data.Finally, the insights gained from this effort can be used to design better network protocols and a betterarchitecture for internetworking of heterogeneous wireless networks. Our first year effort will beconcentrated in the first step – gaining fundamental understanding in various aspects of interoperability.

The coalition scenario places several challenges to the problem. First, the state of art resourceallocation at PHY/MAC layers is based on fixed coordination, policy, and/or coarse grain information,thereby crippling the efficient utilization of resources. Second, ad hoc and multicast routing in battlefieldis challenged by the scale and dynamics of the network, hostile environment, limited node capability, andapplication requirements. Third, interoperation among different networks (e.g. MANETs and sensornetworks) having different goals and policies and has not been understood well in the wireless context.Fourth, testing interoperability of new and existing network protocols for wireless and sensorcommunications requires a lot of manual effort.

This project strives to address these challenges in novel ways investigating both horizontal andvertical interoperation issues. The horizontal interoperation is necessary when we try to interoperateacross different types of networks (e.g. based on different technologies and employing different policies).One example is the case when two wireless networks try to negotiate on physical channels so that they donot interfere with each other. Other examples include routing packets across different network boundaries,and negotiating security and QoS policies among networks. The vertical interoperation issue arises whenwe try to optimize the performance of a network stack by coordinating the operation of a low layerfunction and an upper layer function. For example, if the link layer can provide the information aboutpacket loss (e.g. whether it is due to congestion or fading on the wireless channel), the network layer andtransport layer can make intelligent decision about routing and congestion control. Our team will studythese problems without necessarily relying on existing protocols or internetworking framework although


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we sometimes refer to some of the existing works as examples. Instead, our approach is to characterizethe fundamental limits and bounds of interoperation based on the mathematical models obtained byabstracting the basic functionalities of wireless networks. We will also actively collaborate with Projects 1and 3 to gain insights from their theoretical results.


The project consists of different tasks as described below.

Task 1: Interoperability by Spectrum ScavengingResearchers: Jason Redi (BBN Technologies), Prithwish Basu (BBN Technologies), Nancy Griffeth

(CUNY)

The military has an increasing need for delivering information quickly and efficiently to any place inthe battlespace. Two major impediments for achieving this goal are 1. limited available bandwidth and 2.spectrum deconfliction between entities that do not regularly interact with each other. The limitedavailable spectrum is managed "by hand" in advance of operations, which often results in operators on theground having to be involved in further real-time deconfliction if the operation changes such that newradio hardware or additional assets are involved. One example from Iraq is that when Predator UAVs areused in operation, they often force soldiers to reconfigure some of their own communication gear becauseboth their gear and the UAVs use channels in the Ku band.

Currently spectrum is licensed without regard to actual usage at given times. As a result, a militaryradio is limited to using a just a hundred megahertz a many thousands of megahertz are nearby andunused. We are interested in considering the case of multi-hop networks that perform dynamic spectrumaccess as secondary users in bands potentially occupied by primary, non-cooperating users. Our goal isto maximize the available bandwidth to this secondary multi-hop network, while limiting the amount ofinterference seen by the primary users. We assume that the multi-hop radios can sense wide bands ofinterest and that energy utilization is a secondary concern (but still a concern). We assume that policiesdesignating which bands are politically allowed for transmission already exist and focus on the problemof opportunistically using portions of spectrum.

The Dynamic Spectrum Access (DCA) problem can be divided into two primary topics [6] - DynamicSpectrum Allocation and Opportunistic Spectrum Access (OSA). Dynamic Spectrum Allocation focuseson centrally dynamically allocating spectrum according to need, traffic and spatial characteristics toprimary users. This provides a benefit over existing static techniques but is mostly appropriate forinfrastructure networks [7]. Instead we focus on opportunistic spectrum access. Previous works in MACprotocols that take advantage of opportunistic spectrum sharing have two major components - apredetermined coordination channel to share spectrum sensing information among different MACprotocols, and distribution of a black-white map which allow different nodes to express what bands are inuse. These components exhibit significant limitations that we will work to overcome in our research. Thenext three topics describe the details of what we need to do to provide a general spectrum scavengingcapability.

First, any two OSA nodes need to agree on a common band that can be used for expressing usage orrendezvous information. This can be a very difficult problem for three reasons: (1). It is unlikely that twoadjacent nodes will sense the same usage information and therefore "prefer" the same band forcommunication. (2). The time and energy cost to sense or attempt communication on all possible bandsmay be prohibitive. (3). Most radio systems have a very limited number of transceivers such that a nodecannot listen on all bands simultaneously. This means that it is possible for two nodes to never "find"each other because one is always sending on a band that the other is not listening on. Some previousworks have relied on a pre-existing coordination channel, though this seems to require the exact fixedspectrum allocations that we are trying to avoid. We plan to consider networks where primary users usenon-Poisson traffic statistics, such as packet train models to try to use models as close to reality aspossible. We will begin our work assuming all primary and secondary nodes are synchronized and


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transmit/receive in common time slots, but plan to also consider unsynchronized slotting and CSMAmethods by both the primary and secondary users. An approach for a basic agreement on a coordinationchannel will include exploiting synchronized clocks (such as GPS) to assist in searching patterns betweenpotentially adjacent nodes.

Second, we propose to provide condensed probability distributions that describe the likelihood that aparticular band is in use. This allows coordinating nodes to consider the particular penalty of using aband that is "in use". Even if a band is technically occupied, it may be only occasionally occupied suchthat a sensing node can use this band in a limited context without significantly interfering with existingusers. Also it allows military nodes to balance the priority of their transmissions against existing users. Ifthere is an immediate need to transmit high priority location information of an operating terrorist cell (forexample), the need to distribute such information would presumably overrule any spectrum rights of, e.g.,taxi drivers. We will pursue several research directions. One is to identify how to appropriately cater theamount of information expressed to the ability to use that information within a system. A second is todevelop techniques for constructing the likelihood distributions mentioned above that can adapt tochanges in spectrum usage over time. As with the coordination channel search, we plan to consider non-Poisson traffic that is slotted, unslotted and asynchronous. This makes the representation and expressionof the usage particularly challenging.

Third, we will address is the problem of gaining consensus among nodes. Once we have identified acoordination channel, and expressed spectrum use in each area, it is important gain a consensus of whichbands to use for communication. This is complicated by the complex nature of expressing spectrum useand combining that information to result in useable bands. Consensus is also biased by the expectation ofthe load on the channel. Since different bands may provide different bandwidth, transmit power orpropagation characteristics than others, it is important to select the band appropriate for the system needs.

Task 2: Robust, Reliable, Efficient MulticastResearchers: Mario Gerla (UCLA), Kang-Won Lee (IBM)

We will approach the multicast problem in MANETs in steps, by looking at various requirementsseparately. To guarantee scalability in the presence of mobility, we plan to leverage and extend the lessonslearned from prior schemes. In particular, we exploit the concept of group motion to investigatehierarchical multicast structures where the “leaves” of the tree or mesh are not individual nodes but rathergroups of nodes (or teams) moving together. Thus, intra-team topology management is minimized. Theremaining challenge is to manage the inter-team topology. This “high level” topology, however, is ordersof magnitude smaller than the full system mesh. The multilevel approach also allows easier congestioncontrol and reliable delivery as later discussed. Another powerful approach to scalability in the presenceof mobility is geo-routing. This applies also to systems with very scattered motion (as opposed to groupmotion). In particular, GeoCast provides excellent performance when the nodes know their position (e.g.through GPS) and the multicast is directed to specific regions. The two schemes – group motion and geo-routing - are not antagonistic. Geo-routing can be used, for example, to maintain robust connectivityamong the leaders of fast moving groups. We will evaluate the scalability of these approaches usinganalysis (to establish bounds), and via simulation. One fundamental aspect of routing/multicast that hasnot been sufficiently explored is the joint handling of scalability and mobility, exploiting the synergy ofthe two at advantage. We plan to define performance bounds that tie these properties together and thatreflect the dependence on specific motion models. The next challenge is congestion control. We plan toexplore an application level framework that can run on any multicast structure, including the previouslymentioned multilevel models (e.g. team multicast). This framework will handle both congestion controland data recovery – the congestion control will be driven by packet loss feedback and data recovery willbe handled by local and source retransmission. Based on the loss type (e.g. congestion loss or random lossdue to jamming), source will react differently. For example, if the source detects jamming, it appliessource coding instead of throttling the rate. The design of stable and efficient congestion controltechniques will require the use of analytic tools (such as the “utility function” formulation), and this willbe done in collaboration with PIs in Project 1.


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We will evaluate the scalability of these approaches using analytic tools (to establish bounds), and viasimulation. The next challenge is congestion control. We plan to explore an application level frameworkthat can run on any multicast structure based on a layered model. This framework will handle bothcongestion control and data recovery – the congestion control will be driven by packet loss feedback anddata recovery will be handled by local and source retransmission. Based on the loss type (e.g. congestionloss or random loss (due to jamming)), source will react differently. For example, if the source detectsjamming, it applies source coding instead of throttling the rate. The design of stable and efficientcongestion control techniques will require the use of analytic tools (such as the “utility function”formulation), and this will be done in collaboration with PIs in Project 1.

To enhance efficiency and reliability of multicast we will also explore network coding. In networkcoding, the multicast source injects multiple packets into the network, and each intermediate noderebroadcasts a linear combination of the inputs. Each receiver thus accumulates samples that are linearcombinations of the inputs. Upon receiving enough samples, the source “inverts the matrix” and retrievesthe inputs. Network coding enhances reliability by exploiting network path redundancy. Conventionalnetwork coding scheme, as developed in wired networks, require careful preplanning of paths andprecomputation of mixing coefficients. These schemes are totally inappropriate for mobile networks.Fortunately, recent extensions of network coding theory have shown that asymptotically optimal networkcoding performance can be obtained (for large networks) using random network coding (i.e. randommixing and random, dynamic path selection). Coding “vectors” in the packet headers allow the tracing ofpacket mixing “history” in the network and permit efficient reconstruction of the original packets.Random network coding has been proven to enhance performance (of conventional multicast) especiallyin channel impaired conditions (rapid fades), frequent random losses and scattered motion. Networkcoding scales very well to multiple flows. In fact, it provides extra enhancements if there are flowstraveling in opposite directions. Network coding works best with large files, as it exploits the mixing ofconsecutive packets in the same file. If files are short and real time performance is at premium, networkcoding cannot be applied. The multicast scheme, however, will be intelligent enough to detect the shortfile cases and use other performance enhancing techniques such as redundant transmissions on multiplepaths. We will integrate network coding with application level congestion control and test it in mobilebattlefield scenarios. We plan to develop analytic bounds on the improvement that is achievable withnetwork coding in various channel error and loss conditions. The theoretical aspects of the networkcoding investigation will be closely coordinated with similar efforts proposed in Project 1.

Finally, we will analyze the fundamental characteristics of wireless routing (unicast or multicast) withdifferent degree of information available to the routing layer without necessarily modeling the behavior ofparticular MANET routing algorithms. For example, without much information about the mobility andthe location information, the route discovery and packet forwarding must rely on flooding. However, withlocation/mobility information available via GPS or inertia detection devices, routing algorithms can domuch better even with a reduced scope of broadcast. However, this approach can also increase the chanceof not finding the routes even when the network is in fact connected. We will analyze the fundamentaltrade-off between the effectiveness of routing mechanism and the overhead incurred. We will also lookinto the case where different types of routing algorithms must interoperate with each. This will be done incollaboration with other researchers in task 3.

Task 3: Inter-domain Wireless RoutingResearchers: Jon Crowcroft(Cambridge University), Robert Hancock(Roke Manor Research)

The goal of this work is to understand how to architect inter-working between heterogeneousMANET domains. Some may be sensor networks, some may include fixed wireless links and access toinfrastructure; others may be classical MANETs on personnel, vehicles on land, or in the air. EachMANET will have a routing sub-system optimized for the capabilities of nodes, the radios available, andthe context (application set, mobility, and RF propagation). The radios may be adaptable (softwaredefined) and may use local as well as disseminated information to configure themselves.


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At the boundaries between domains, a number of functions are required which may go beyond theclassic inter-domain routing functions. These include: policy control for ingress, egress and transit;interface to spectrum allocation policy; use of appropriate mechanisms for QoS; adaptation to differentcapabilities for congestion control. The complexity of systems composed of multiple heterogeneousMANETs can be reduced by hierarchy and aggregation, just as in fixed wired networks. However, wewould like this decomposition to be correct and efficient by design.

In this work we intend to continue prior architectural thinking based on our Plutarch approach at thepractical design level. In that context, we will develop an application of the metarouting approach to theinter-domain MANET problem. The key output from this will be the framework within which one can testinter-domain MANET routing systems for convergence and resilience. A benefit of taking the principledapproach is that, assuming that the inputs on policy and security are correct, the resulting systems obeypolicy controls as well. We intend to research whether resilience, e.g. to DDoS attack of the routingsystem or end user nodes, can also be deduced formally using the metarouting framework. Themetarouting framework has recently been extended to cover networks with non-strict preferences, e.g.multi-path routing, traffic engineering, QoS routing and VPNs. This gives a starting point for extendingthe approach to interconnected MANETs in the way described.

Task 4: Composable NetworksResearchers: Srivatsan Varadrajan(Honeywell), Robert Hancock(Roke Manor Research)

In this task, we aim to develop a theory of composable networks. The approach involves composingnon-intrusive controls across a federated network to manage end-to-end performance and controloverheads in a well-defined framework. Broadly, we define four subtasks.

ST1: Objective Functions and Optimization: We want to formulate the composition as anoptimization problem with an objective function and constraint. The objective function should specify thegoals of network nodes, individual networks, and the federated networks. At the same time it shouldcapture the changing requirements and resource availability, such as (a) end-to-end utility; (b) applicationclass, (c) priorities of nodes; and (d) resource usage (e.g. energy consumption). On the other hand,operating constraints should consider network uptimes, connectivity, routing policies, etc. The objectivefunction should be sensitive to changes and situational dynamics such as user requirements, QoS changefor applications, and changes in mission objectives. Note that our goal is not to achieve the absoluteoptimum for a given objective function and the load. There may be a monolithic system design that canachieve that; however, such an approach does not generalize to interoperation among heterogeneousnetworks. Instead, we will study the tradeoff that has to be made in moving towards an abstractrepresentation of the component network: as the abstraction becomes more high level, the optimizationfor each individual network becomes less perfect, but the internetwork optimization problem becomesmore tractable.

ST2: Decomposition of Network Protocols: At a fundamental level we model a single network as ablack box (i.e. the basic component). We propose to solve the optimization problem by architectingindividual networks to be interoperable in a control theoretic fashion, i.e. we need interfaces to change thecontrol variables and extract information from the monitored variables across all layers of the protocolstack. We design the protocol for parametric optimizability so that the behavior of protocol can becontrolled by using appropriate parameters to achieve certain optimality goals. We can for exampleconsider MAC transmit power, Hello intervals in the routing protocol to be control parameters and MACretries and neighborhood discovery time to be monitoring variables. In conjunction, we need tocharacterize the overheads associated with measurements and give the ability for the optimizer to controlthem. Our approach would be to start with a simple model of a component network as connectivity andtraffic matrices, where the data transfer requirements are described at the internetwork layer; the responsefunction must describe how a component network satisfies these requirements in terms of a multipartresource metric (number of nodes, network endurance, occupied spectrum). We begin by using a simpleparametrizable model of MAC layer operation to provide an initial example of such a response function,and to define how to specify the interaction between the input connectivity/traffic requirements and the


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configuration of the lower layers. Later work (included in ST4), will generalize this response function andinteraction model using inputs primarily from Project 1.

ST3: Managing the Component Networks: The optimization process operates across the network ofnetworks, taking as inputs the overall traffic and connectivity requirements and the abstracted capabilitiesof each component network. The initial analysis will consider that traffic at the inter-network boundariesis constrained to a simple connectionless packet interface, with inter-network routing being an externalinput and not subject to the optimization process. This will provide a minimal robust solution which alsohandles the necessity for interconnection with legacy networks. We will then assess two enhancements tothis model: first, one that merges the inter-network user plane across layers (transport and below) andreorganizes it based on functionality, helping to reduce protocol overheads; and second where theoptimizer interacts and modifies (as needed) the inter-domain routing configuration. This will includeinteraction with the work of Task 3. Next, we need guidelines for policies relating to knowledgemanagement and information sharing as well as protection. We plan to collaborate with Project 4 indesigning a policy framework to enable this sharing and protection. Finally a cooperative negotiatingframework is also needed when a decision must be made on the fly in a coordinated manner.

ST4: Develop the Optimization Theory and Algorithms for Adaptation Behavior: ST4 is responsiblefor the actual development of the optimization algorithms and a comprehensive optimization theory. It isexpected that in the first year, only initial steps can be made in this direction. Primarily, these will consistof the definition of the simulation and analysis framework and its application to a set of referencescenarios. The first year optimization theory will evaluate algorithms for distributed optimization at theinternetwork level using a basic model of component network behavior as described above in ST2, ST3and simple objective functions developed in ST1; it will also evaluate the results from Project 1 as a basisfor more fundamental and robust component network models.

Task 5: Testing Interoperation of Heterogeneous Wireless and Sensor NetworksResearchers: Nancy Griffeth (CUNY), Jason Redi (BBN), Prithwish Basu(BBN)

The fundamental issues addressed in this task are testing heterogeneous wireless and sensor networksprior to deployment to verify that they will interoperate as desired in the field, and designing wireless andsensor networks for testability. The kinds of testing addressed will include testing network services suchas resource location and database access as well as testing protocols. The research will apply aninnovative approach to network testing, which is being developed under NSF funding, to the JAVeLENmobile nodes that have been developed at BBN and also to simulations of wireless networks available forns-2 and OPNET.

Outcomes of the work will include:1. Evaluation of properties of proposed algorithms for wireless networks, such as limits on the

number of nodes or limits on the delays in a network for the algorithm to work correctly;2. Evaluation of properties of specific algorithms used in the BBN JAVeLEN nodes;3. Creation of a variety of models based on various aspects of the behavior of the BBN testbed

network and on aspects of the behavior of simulations.4. A test suite for mobile nodes; and5. An evaluation of approaches to network testing and recommendations for such testing.

Current best practice in network testing addresses only testing protocols and requires verifying thecorrectness of individual network protocols based on a manually created, detailed model of the protocols.Before use, the models must also be validated against required properties such as “addresses must beunique within a network” and “routing algorithms must avoid cycles.” Testing interoperation ofheterogeneous networks requires new approaches, because the networks may fail to interoperate eventhough all protocols are implemented correctly. Furthermore, detailed, validated models of individualprotocols are hard to develop and rarely available.

Because the network protocols and services to be used in military sensor and ad hoc networks are stillunder development, initial experiments will use the BBN testbed mentioned above and availablesimulations of wireless services and protocols. The approach that we use for interoperability testing is


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based on the tool suite AGATE [1], which automatically generates state machine models based onobserved network traces. The models are expressed as timed I/O automata [3]. Actually, the tools createseveral timed I/O automata models of a network from network traces, each focusing on a different aspectof network behavior. TCP, for example, has components for managing session set-up and teardown, flowcontrol, congestion control, and reliable delivery of messages. To make the state machines tractable, wereduce the modeling problem by breaking it into separate components such as these. For this research, wewould be decomposing wireless network traces into component activities. In task 1, we propose toaddress spectrum sharing; that would make a good candidate for a component state machine model.Subsequently, all the techniques available to study correctness and performance using TIOA would beavailable. For example, another TIOA-based tool is a test driver. We use tools such as PVS [2] forproving properties of the models and Monte-Carlo model-checking finding counter-examples to theproperties.

Using the decomposition technique, we can also address testing services, such as resource location,and not just the supporting protocols, on heterogeneous interoperating wireless networks. Since statemachine models are constructed automatically from network observations, it is feasible to model activitiesspanning multiple protocols. The models are based on a specific network, but, using additionalinformation from the specification, they can be generalized to allow study of various network topologiesand configurations


The contributions of this project to the US and UK military will be manifold. First, we willinvestigate novel PHY/MAC layer techniques to dynamically identify unused spectrums to maximize thecommunication capability of a coalition force without requiring manual coordination. We will also studyhow the MAC can exploit the MIMO scheme underneath it to optimize the performance. Second, we willdesign multicast communication schemes for various type of application in such a way that it can adapt todynamically changing urban tactical scenario. This is particularly important since multicast is thepredominant form of communications in the battlefield, from commands to situation info dissemination.Third, we will investigate various ways to interconnect heterogeneous networks from PHY/MAC toapplication layers while satisfying various QoS and security requirements. To facilitate this process, wewill develop a theory of inter-domain wireless routing and composable networks, which allows a coalitionforce to build a large scale network based on their optimization goal and security and other policies.Finally, we will address the challenges posed by the need to test the correct and effective interoperation ofvarious wireless and sensor network protocols, both old and new. By addressing these issues we willimprove along the various key axes of network centric warfare including coalition force formation,operation tempo, information reachability, shared awareness, and degree of collaboration.


We have several collaboration plans at hand both international and cross-organizational. NancyGriffeth (CUNY) is collaborating with BBN (Jason Redi and Prithwish Basu) on MAC-layer algorithms.Griffeth is also considering collaboration with Mario Gerla or Jon Crowcroft. One graduate student fromCUNY will be partially paid by this project and could spend 1-2 months at another location. Mario Gerla(UCLA) anticipates collaborations with Researchers in the MIMO tasks and in the theory of networking(Don Towsley). He will seek the opportunity to send one of the two Research Assistants working on thistask to IBM for a spring 2007 Internship and to collaborate with ««GreetingLine»»Lee (IBM). RobertHancock (RMR) will collaborate with Srivatsan Varadarajan (Honeywell) on the Composable Networkstopic, and will seek to collaborate with Jon Crowcroft (Cambridge) in the inter-domain wireless task.

There are natural collaboration opportunities with Project 1 (e.g. utility function, MIMO and MACtechnology, multi-path data delivery) and Project 3 (e.g. self-organization, resilient and fault-tolerantnetwork design). We also seek to collaborate with Policy-based Security Management in the composablenetwork task. For example, we will investigate together to identify ways of linking the security policy (i.e.


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these two devices are authorized to communicate) to the device configuration (i.e. these two devices cancommunicate). Another example will be to address the dynamic tactical environment in which devicesencounter situations not anticipated by the security policy.


The work in the spectrum scavenging task builds on work originally done in the DARPA XGprogram. The DARPA Connectionless networks program uses a radio with multiple channel capability,and therefore could benefit from practical results from this research.

The effort of Gerla (UCLA) is related to ARMY MURI project DAWNS (J. J. Garcia, UCSC PI); theMURI project is manly concerned with realistic mobility and scalability models for unicast and multicastrouting. The ITA project is complementary in that it is specifically attacks multicasts and addresses cross-layer optimization and Network Coding.

Griffeth (CUNY) is also funded on an AFOSR grant for developing the test tools and by Cisco to testthe DHCP failover protocol. On the ITA collaboration, she will address how to test wireless and sensornetworks, which she is not addressing in other work.

Published industry work on testing networks has been initiated primarily by the telecommunicationsindustry and presupposes a detailed model of the network before the network can be tested. Because suchmodels are almost never available until after protocols have been implemented, actual application of thiswork to testing real networks is rare. The best applications of this work involve deriving models fromsoftware and developing models and software in parallel. The prime conferences for this work areFORTE/PSTV and TESTCOM.


Research Milestones


Q1 Task 1 Development of algorithms for providing rendezvous capabilitywithin large distributed state space (BBN, CUNY)

Q1 Task 2 Scalable Multicast – report describing the impact of mobility andlarge scale on multicast performance. Literature overview. Resultsfrom the proposed scaling approaches. Paper submissions thatsummarize the above results (UCLA)

Q1 Task 3 Information gathering on different MANET domains. Discussionwith groups on component architectures for MANETs. Discussion onpolicy and security requirements, and potential for differences acrosscontexts.

Output will be documentation of the requirements (Cambridge).

Q1 Task 4 Primarily developing project plans. Report with preliminaryrequirements for subtasks ST1, ST2, ST3 and ST4 (Honeywell/RMR).

Q1 Task 5 Determine initial test suite for JAVeLEN nodes. Prepareconfiguration files for AGATE tool suite, to run tests and evaluatesimulations. Define essential properties from node requirements.Report on service selected for study and appropriate measures oftesting and monitoring effectiveness for testing and monitoringheterogeneous wireless and sensor networks (CUNY).


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Q2 Task 1 Development of methods to express spectrum utilizationappropriate to the need and capability (BBN, CUNY)

Q2 Task 2 Reliable Multicast - report describing the importance of reliablemulticast and the tradeoff between reliability and delay (in both realtime and data applications). Impact of mobility and large scale onreliable multicast performance. Literature overview. Results from theproposed reliable support approaches. Paper submissions thatsummarize the above results (UCLA)

Q2 Task 3 Use of metarouting to describe at least 1 inter-domain scenario.

Input from specific existing (or developed) MANET routingschemes (Cambridge).

Q2 Task 4 Report relating to sub tasks ST1, ST2, ST3, ST4

Detailed requirements

Feedback/Validation of requirements

Develop federated network use-case scenarios applicable forcomposable networks

Survey of research literature of associated technologies(Honeywell/RMR)

Q2 Task 5 Complete initial test cycle of JAVeLEN nodes and simulation,creating TIOA model of service and algorithms for test driver.Configure test driver based on TIOA state machine output.(CUNY/BBN)

Q3 Task 1 Development of algorithms for gaining consensus of usablespectrum (BBN, CUNY)

Q3 Task 2 Network coding – report in which we review the application ofNC to multicast; modeling and simulation results. Analytic bounds onperformance. Impact of Network Coding on reliable multicastperformance. Interaction of NC with lower layer protocols (e.g.MIMO and “link level” coding). Literature overview. Papersubmission that summarize the above results (UCLA)

Q3 Task 3 Discussion on possible interface between metarouting andspectrum allocation.

Start on specification of simulation or emulation framework(Cambridge).

Q3 Task 4 A report which includes:

Formulating the Problem statement in ST1

Preliminary designs and idea development for ST2, ST3. Developevaluation criteria for ST2 and ST3.

Details of basic simulation and/or analysis tool capabilities ST4(Honeywell/RMR).

Q3 Task 5 Complete second round of testing of JAVeLEN nodes. Developproofs or counter-examples from PVS and model-checking, based on


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the results. (CUNY,BBN)

Q4 Task 1 Conference paper with results from spectrum scavenging tasks(BBN, CUNY)

Q4 Task 2 Multicast congestion control - Utility function approach to definethe tradeoffs between throughput, delay and fairness. Reportsummarizing the utility function formulation. Report on preliminarysimulation results on the feedback control mechanisms inspired by theutility function approach

(UCLA).

Q4 Task 3 Initial thoughts on formal output from metarouting work toautomate specification of testing work, to be deployed at domainboundaries, e.g. for convergence after outages or attacks (Cambridge).

Q4 Task 4 Report/publication relating to one of the problems (or toned downtractable version of it) in ST4 applicable to some of the federatednetwork scenarios developed in Q2. Also show preliminary simulationand/or analysis results (Honeywell/RMR).

Q4 Task 5 Report on evaluation of test methodologies and makerecommendations (CUNY).

5.7. References

1. Constantinos Djouvas, Nancy Griffeth, and Nancy Lynch, “Testing Self-Similar Networks,” inWorkshop on Model-Based Testing at ETAPS 2006, Vienna, Austria, March 26-27, 2006.

2. S. Owre, J. M. Rushby, , and N. Shankar. PVS: A prototype verification system. In Deepak Kapur,editor, 11th International Conference on Automated Deduction (CADE), volume 607 of LectureNotes in Artificial Intelligence, pages 748–752, Saratoga, NY, Jun 1992. Springer-Verlag.

3. Nancy Lynch. Distributed Algorithms. Prentice-Hall, 1996.4. D. Lee and M. Yannakakis. Principles and Methods of Testing Finite State Machines - A Survey.

Proceedings of the IEEE, volume 84, August 1996, 1090-1126.5. J. Park, D. Lun, Y. Yi, M. Gerla, M. Médard. “CodeCast: A Network Coding based Ad hoc Multicast

Protocol”, in IEEE Wireless Communications, Oct 2006.6. Q. Zhao, L. Tong, and A. Swami. "Decentralized Cognitive Mac for Dynamic Spectrum

Access", Proceedings of IEEE DySpan 2005.7. V. Brik, E. Rozner, S. Banerjee, P. Bahl. "DSAP: A Protocol for Coordinated Spectrum

Access", Proceedings of IEEE DySpan 2005.


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6. Project 3: Biologically Inspired Self Management of Networks

Project Champion: Pietro Lio’, University of Cambridge Affiliation



David Chess, IBM Erol Gelenbe, Imperial College

Xiao Zhen, IBM M. Gerla (UCLA)

Srinivasan Seshan, CMU Jim Kurose, UMass-Amherst

M. West (RMR) Don Towsley, UMass-Amherst

Jon Crowcroft, Cambridge University Tom McCutcheon, DSTL

Pietro Lio, Cambridge University Brian Sadler, ARL


The aim of Project 3 is to develop network architectures that are robust and self organizing based onbiological systems and that will provide robust communications without relying on either excessivehuman expertise or control, or complex software. The aim is also to investigate biological systems whichexhibit self-organization and those properties that we desire in an ad-hoc network.


Task 1: Self-organization and desired properties in an ad-hoc network.Researchers: Pietro Lio, Cambridge University, Jon Crowcroft, University of Cambridge, Zhen Xiao,

IBM, David Chess, IBM.

Networks are the fundamental paradigm of several areas of biology, such as epidemiology, immunesystem response to pathogens, genetic and system biology regulatory networks, networks of neurons,social networks in ecology.

Biological systems such as the brain, immune, and genetic networks suggest new ways of identifyinga set of nodes in a network that, if removed, maximally disrupts communication among remaining nodesand how efficiency may be compromised in networks with a high degree of centralization. We plan toanalyze such designs and use the results of such analyses to ensure that our system produces topologiesthat are robust to failure. Similarly, past research on social networks tells us how the positions of differentindividuals within them affect their robustness and potential actions. We believe that these observationscan help us design network topologies that reflect the roles of the different users/participants in thecommand hierarchy of a military network.

Networks in biological systems are inherently simple: they are modular, where modularity is definedas partitioning of the design into units that perform independently, at least to a first approximation.Furthermore, they reuse certain circuit patterns, termed network motifs, in many different parts of thesystem. These patterns allow the construction of extremely complex systems by using simple building

The members of Project 3 have developed or have publication and patent records showing the ability todevelop techniques to enable self-organization, information dissemination, topology mangement andspectrum management in wireless networks [1][2][3]. The project uses biologically inspired designs toivestigate two directions: self-organization and desired properties in an ad-hoc network wireless topologymanagement.


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blocks. We plan to leverage such concepts to manage the complexity of an integrated design for self-organization, topology management, and spectrum management.

A key feature of a biological network is its ability to adapt to environmental changes by evolving in amodular fashion. This property permits designs with higher modularity to exhibit higher adaptability andtherefore higher survival rates in changing environments. Recent works have suggested that biologicalnetworks may switch between several goals, each composed of a different combination of subgoals. Sucha "modularly varying goal" paradigm leads to the spontaneous evolution of modular network structuresand network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Suchswitching between related goals may represent biological evolution in a changing environment thatrequires different combinations of a set of basic biological functions. Several studies suggest thatmodularity spontaneously arises from duplication of subsystems, as we see in genetic networks orselection for stability or robustness, or under changing environments. One of the challenges that we facein our own system design is managing the interaction between different parts of a system adapting toenvironmental changes. Therefore, a major effort will be placed on the analysis of dynamical properties ofadaptive networks at different scales (analysis of topology adaptiveness in the presence of differentdynamics at node and motif levels), in response to environmental challenges (epidemics, fluctuatingenvironments). This will allow the development of improved and effective stochastic models of wirelessperformance.

In the first year of the project, we will use dynamical system theory and mathematical modeling tocharacterize those dynamical properties of biological systems which are more relevant to ad-hoc networkdesign. In particular we will focus on epidemic modeling, which has been successfully bridging biologyand computer networks. We then aim to develop bioinformatics algorithms (particularly in the area ofmachine learning and including specific visualization tools) to analyze biological networks as a paradigmfor wireless data transfer.

First year activity: Epidemiology, networks, and information dissemination

One promising area of biology that has direct application to military wireless networks isepidemiology. Situations often arise where a message needs to be either disseminated to one or moreusers or broadcast to the entire population with great urgency. Epidemic style routing [1][2], where thesource transfers the data to any node that it encounters and subsequent nodes do the same, is a naturalapproach to this problem. This process is very similar to the spread of an epidemic for which biologistshave developed a rich set of models stemming from the classical SIR model [4][5][6]. The simplest ofthese is a deterministic model where the number of infected individuals over time is described by adifferential equation leading to an explicit solution. In the context of epidemic style routing, this solutioncan be used to approximate the distribution of the message delivery time to all intended receivers and thedistribution of the number of copies of the message in the system at time t, t > 0. We propose to studyepidemic-style routing algorithms borrowing as needed from the work on epidemics [7]. See also [8].Within this context, we will account for scarce resource constraints (capacity, energy, memory). We willfocus on the questions described below:

What are the performance limits of this approach?A specific problem that we will study is the following: to analyze a system that is constrained to

make no more than k copies of a message as a way of studying the tradeoff between delivery time andoverhead (number of copies). The time to transfer the data to a user is bounded from below by that of apolicy that disseminates copies any time that a user containing a copy encounters one lacking a copy.This is not a realizable policy, and hence provides a bound. We will use both Markov models and thedifferential equation approach mentioned above.


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What policies approach the above fundamental limits?As part of our work, we will study realizable policies, such as spray and wait [2], which may perform

close to the limits obtained while answering the previous question. Here, spray and wait is a technique inwhich there are k tokens in the system at all times where one token is associated with a one message copy.The sender initially starts with k tokens and one message copy. Tokens and message copies are distributeduntil each token has been distributed to a different user along with an associated message copy.Eventually one of these users will meet the receiver and the message will be delivered. We conjecture thatsuch a policy is near optimal. We will apply sample path techniques [9] to show this to be true.

How to distribute and monitor security management policies in networks?This is relevant to security management and to identify the optimal conditions for monitoring the

percolation patterns of updates of policies, and in general, the situational awareness in the networks. Theupdating of the situational awareness can be modeled using stochastic simulation on a variety ofconsistent topologies and protocols. This approach is related to the bio-inspired Gossip spreading andwould lead to the design of estimators of situational awareness for regions of the networks with differentconnectivity, assortativity and superspreadear/assorbant properties.

These mobile networks cannot be represented by static graphs. Instead they are better represented bydynamic graphs that are constantly changing over time. As part of our work, we will explore what theright model of the underlying graph is. As this matures, the results will be folded into the above proposedwork.

Epidemiology has an even more direct application to understanding the spread of malware (malicioussoftware such as worms and e-mail viruses) within a wireless network. In earlier work authors havefocused on the following question: what effect does topology have on the spread of a virus Amongstudied models were the SIS model where infected individuals can recover but then become reinfectedand determined that the spectral properties of the adjacency matrix of the network topology play a keyrole in determining whether a virus is harmless (dies out quickly) or harmful (stays around for a longtime). In the context of a wireless network, the SIR model, where infected individuals recover but cannotbe reinfected is more natural. A meaningful way to incorporate knowledge from biology into real wirelessmodels is the use of Bayesian statistics and other machine learning methods. This is particularly true foropportunistic networks where we evaluate any possible contact to send/receive data. For example we canderive a distribution of behaviors/histories of infections that we may use as prior probability (in aBayesian meaning) to decide whether to trust a new contact and for how long or consider it infected. Thisapproach is not necessarily computationally heavy and/or time consuming since the statistics can be pre-computed based on the history of the ensemble of users. It is a tradeoff of the time used to trust the newarrival and the time you have to use the guy to send/receive

In conclusion, we propose to address wireless networks using our expertise in Bayesian networkmethods in combination with our expertise in biological systems.

Task 2: Wireless Topology ManagementResearchers: Srinivasan Seshan (CMU), Mark West (RMR)

Over the next several years, we plan to develop, analyze, implement and experimentally evaluatealgorithms for managing the configuration settings (e.g., transmission frequency, transmission power, andantenna configuration) of wireless nodes in a coalition environment. This configuration, in turn,determines the topology of the network created by these nodes. Our plan is to use biologically inspiredtarget topologies as well as biologically inspired control algorithms for setting the configurationparameters. Our belief is that developing an optimal solution to the joint optimization of self-organization,topology management, and spectrum management is intractable. Within the context of ITA we expect todeal with wireless nodes that may be fixed or mobile; energy rich or poor; and present a variety of


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requirements to the network to which they belong. Due to the complex interaction between nodes and thedynamic nature of the environment (e.g., due to mobility and changes in propagation properties), it isunlikely that there exists any long-lived optimal configuration exists and even a short-lived configurationmay be difficult to compute. Further, the interactions between decisions made in each of these threedimensions are quite complex and unlikely to be analyzed jointly. To avoid this problem, we target robustand predictable behavior rather than optimality. In addition, we plan to carefully explore the interactionsbetween decisions made at these different layers and plan to develop design principles that avoid adverseinteractions. We plan to draw inspiration from a number of areas to develop algorithms for the differentlayers that work well together. This includes areas such as control theory, artificial intelligence, biologicalnetworking and social networking.

Robustness and predictability has many dimensions [10][11]. For example, in constructing atopology, we must tolerate node failures, node mobility, differences in traffic patterns and differences inapplication requirements. We describe the challenges in addressing some of these different dimensionsbelow.

Biological interconnects/relationships often exhibit a high degree of robustness to individual failuresor unexpected behavior. In making topology management highly robust to the failure of individual nodes,we plan to borrow many ideas from biology. For example, power-law relationships which frequentlyappear in biological systems also appear in Internet-like topologies. Such topologies have nice propertiessuch as the availability of several good alternate paths from a source and destination. However, usingsuch a power-law topology also requires that the self-organization layer be able to take advantage of thisredundancy in the topology and the spectrum management layer to appropriately allocate spectrum toensure good performance along all these additional links. We also plan to explore the construction ofmore regular topologies that may have nice provable properties.

Challenging issues also arise in providing good performance for the typical application workload. Forexample, each node must select a transmission range for its radio; this property defines the coverage ofthe network – i.e. where nodes appear or move to and maintain connectivity. We plan to explorealgorithms where nodes cooperate while shrinking and expanding their transmission ranges to ensure thatthey continue to provide complete connectivity coverage. To take advantage of this topology generation,the self-organization and spectrum management layers must be designed carefully as well. For example,the self-organization layer must be able to understand the properties of the different links and route datasuch that it optimizes for the differing application requirements. We plan to explore diverse networktechnologies (e.g., UWB, MIMO, directional antennas) in developing capacity limits, and to support suchtechnologies in our biologically-inspired designs.

First year activity: Measuring Realistic Environments and Candidate Algorithms

In the first year of the project, we plan to focus on understanding the cross-layer interactions thatoccur in doing topology management, building up an analytical framework for topology management, andidentifying a collection of biologically-inspired algorithms for further evaluation. As a result, we plan toexplore the following questions in this task:

What are the properties of modern hardware and settings? We plan to measure a number ofproperties of modern hardware and environments that are likely to impact our algorithms’ behavior.Examples of properties include: how long devices take to reconfigure, how accurately devicesreport/measure signal reception properties (e.g., RSSI), how precisely devices control transmissionproperties (e.g., transmission power), and how quickly typical the signal propagation environmentchanges.

What characteristics can a topology be optimized for?


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Examples include simplicity, robustness, maximizing battery life, maximizing performance, ormaximizing predictability. Understanding what aspects of topology are important to these differentcriteria help to solve the core optimization problem of this task. Lessons about what makes atopology good and the impact of topology on different performance goals can be informed from thestudy of social and biological networks. We will perform a simulation and analysis based study ofdifferent algorithms and their effectiveness at optimizing for different criteria.

An important aspect of our core goal is to support the heterogeneity that is likely to be presentin coalition settings. Different nodes/applications/coalition members are likely to have differentoptimization goals. However, due to the shared nature of spectrum resources, topology optimizationmust accommodate these various optimization goals simultaneously. We plan to defer exploringsuch heterogeneous settings to later years of the project. In our first year, we do plan to considermore complex optimization goals in scenarios where the topology is more restricted (e.g., innetworks that largely use fixed infrastructure).

What biological approaches could be used and what advantages can such a biological approachbring? Within Project 3, we will work with the various experts to identify possible mechanisms formanaging network topology. Ideal concepts include those which consider global optimizationthrough the use of local decisions. Mechanisms for distributing simple bits of information toimprove decision making and, in extreme cases, simple models that lead to emergent behaviormight be considered.

The identified candidate approaches need to be refined into useable algorithms and analyzedagainst the sort of networking problems that are at the heart of ITA. This analysis will attempt toquantify the sort of improvement that such techniques can offer over traditional methods.


Algorithms and techniques developed in the context of this project will reduced networkadministration overhead in field as well as enables automated organization of networks to best meet theneeds of a military operation.

Biologically inspired techniques are a high-risk high-reward area of research. The incorporation ofbiological concepts into wireless military networks is in a stage of infancy. We hope to make strides alongthat direction, and harness the power of biological techniques to improve computer communications.


Don Towsley (Umass) will collaborate with Pietro Lio’ and Jon Crowcroft (Cambridge University)ON Task 1; Srinivasan Seshan (CMU), Mike West (RMR), Jon Crowcroft and Pietro Lio’ will collaborateon Task 2.


There is work being undertaken as part of the DARPA DTN program on routing in disruption tolerantnetworks. However, the primary focus of the program is on building a working system, not on exploringthe performance limits or developing a biologically inspired framework within which to understand anddesign such a network.


Research Milestones



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Q2 Task 1 Report on message delivery policies. Submission to ACM Mobicom(Cambridge, IBM)

Q2 Task 2 Report describing attributes of network topologies and characteristics ofoptimized networks (RMR)

Report detailing candidate biological algorithms and a preliminary evaluationof their advantages and disadvantages (RMR).

Measurement of common device properties. (CMU)

Q3 Task 2 Complete analysis of suitability and potential benefit from candidatealgorithm(s). (CMU, RMR)

Measurement of common environmental conditions. (CMU)

Q4 Task 1 Report on dynamic graph models with application to wireless networks.(Cambridge, UMass)

Q4 Task 2 Analysis of a specific biologically inspired algorithms for topology. (CMU,RMR)

6.7. References

1. X. Zhang, G. Neglia, J. Kurose, D. Towsley. "Performance Modeling of Epidemic Routing",Proceedings Networking 2006, May 2006.

2. X. Zhang, G. Neglia, J. Kurose, D. Towsley. “On the Benefits of Random Linear Coding for UnicastApplications in Disruption Tolerant Networks.” Proceedings of IEEE NetCod, 2006.

3. A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, J. Scott. Impact of human mobility on thedesign of opportunistic forwarding algorithms Proceedings INFOCOM 2006, April 2006.

4. W. O. Kermack, and A. G McKendrick,. A Contribution to the Mathematical Theory ofEpidemics. Proc. Roy. Soc. Lond. A 115, 700-721, 1927.

5. J.D Murray, Mathematical Biology: II. Spatial Models and Biomedical Applications (811 pages)2003 (second printings 2004).

6. Sguanci L, Liò P, and Bagnoli F The influence of risk perception in epidemics: a cellular agentmodel ACRI2006, Cellular Automata for Research and Industry, Perpignan Arxiv preprint q-bio.PE/0607010, 2006

7. G. Pei, M. Gerla, and T.-W. Chen. Fisheye State Routing: A Routing Scheme for Ad HocWireless Networks. Proceedings of the IEEE International Conference on Communications,pages 70--74, New Orleans, LA, June 2000. http://citeseer.ist.psu.edu/article/pei00fisheye.

8. J. Burgess, B. Gallagher, D. Jensen, B.N. Levine, MaxProp: Routing for Vehicle-BasedDisruption-Tolerant Networking, Proceedings IEEE Infocom 2006. April 2006

9. Z. Liu, P. Nain, D. Towsley, ``Sample path methods in the control of queues'', QueueingSystems, 21, 293-335, 1995.

10. A.Bharambe , M.Agrawal, and S.Seshan. Mercury: Supporting scalable multi-attribute rangequeries. In ACM Sigcomm (2004).

11. A Akella, G Judd, S. Seshan, P. Steenkiste, SelfManagement in Chaotic Wireless DeploymentsMobiCom’05, August 28–September 2, 2005, Cologne, Germany.

http://citeseer.ist.psu.edu/article/pei00fisheye

http://prisms.cs.umass.edu/brian/pubs/burgess.infocom2006.pdf

http://prisms.cs.umass.edu/brian/pubs/burgess.infocom2006.pdf


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7. Project 4: Policy based Security Management

Project Champion: Tom Markham, Honeywell

Email: [email protected] Phone: 612 951-7354


Steve Bellovin, Columbia University Kang-Won Lee, IBM – Project 2

Tom Markham, Honeywell Kenny Patterson, Royal Holloway – Project 5

Kirk Schloegel, Honeywell Dakshi Agrawal, IBM – Project 6

Jorge Lobo, IBM Greg Cirincione, ARL

Seraphin Calo, IBM Trevor Benjamin, DSTL

Morris Sloman, Imperial College

Emil Lupu, Imperial College

Arosha Bandara, Imperial College

Sara Farmer, SEA


The Policy Based Security Management (PBSM) project develops technologies to provide adaptivesecurity for support of a complex system-of-systems, where all systems must maintain secure operationwhile the underlying network-of-networks self-organizes. This must be accomplished during highlymobile and dynamic missions, with unreliable and intermittent connectivity, without centralized securityservices, and with a significant risk of node capture or subversion.

The research issues include: Supporting non-technical users in specifying policy: Military personnel will not generally have

high computing expertise, so they must be able to specify policies at a high level of abstraction interms of natural language goals or via a suitable graphical interface. These abstract policies mustbe refined into a consistent and valid set of configuration settings in network devices (servers,mobile platforms, routers, etc.). It will be necessary to cater for a range of user interfaces fromdesk-top workstations to simple portable PDA like devices used to specify and change policiesunder operational conditions.

Flexible conditional policies: Typical security policies in this environment will not be simplebinary decisions of whether to permit or deny an information flow based on classification labelsor user identity. They need to be able to cater for complex security management decisions basedon current context, available resources, specific operational benefits, current risk and levels oftrust related to infrastructure as well as interacting entities.

Merging policy from multiple sources: The capability to interpret policies defined by a singleadministrator within an organization and push it down into devices is within the state of the art.However, even within a single military organization, policies will be specified and modified bymany different personnel although probably using the same tools and techniques. These policiesmay have overlapping application and have to be merged to form a consistent set ofimplementable policies. However it is also necessary to cater for merging of policies specifiedusing different toolsets by multiple coalition partners into a consistent, unified policy set that is


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then deployed into heterogeneous devices owned by coalition partners. There will be a need fornegotiation to resolve some conflicts which will arise from the merging of policies.

Updating policy in a dynamic disconnected environment: Current policy models and tools can setpolicy in a static, connected environment. The MANET environment is far more dynamic, withdevices joining and leaving the network over timescales of minutes. They may have intermittentwireless connectivity and there is a need to rapidly adapt policies to current context andoperational conditions in which there is no connection to centralized policy tools.

To illustrate the technical challenges in addressing these issues, consider conflict detection andresolution, one of the fundamental research issues in the policy area. Typical algorithms proposed in theliterature use propositional logic to formulate conflict detection problems. Implementations of thenecessary operations are usually static (i.e., performed before policy deployment) and cannot adapt torapidly varying operating environments. Existing work also typically assumes that all policy refinementand analysis is centralized on a comparatively powerful workstation. In contrast to these assumptions inthe majority of existing literature, the key challenges in the MANET environment include:

Decentralization: Conflict resolution for distributed policies would require decentralizedresolution methods. Similarly refinement and dissemination of policies needs to bedecentralized.

Mobility: Mobile devices make the network composition dynamic with intermittentcommunication capability. As a result, the operating environment is highly dynamic making itvery difficult to maintain consistency amongst policies deployed in mobile devices.

Exposure: Wireless communication is more easily susceptible to attacks and mobile devices canmore easily be captured and compromised before being reinserted into the MANET.

Resource Constraints: Many devices have limited energy, processing power, storage, andbandwidth.

The goal of this research work is to address the abovementioned research issues and technicalchallenges. The innovations are expected to mirror the challenges faced by a coalition operating aMANET in a dynamic environment. Thus, the policy innovations are expected to support dynamic policywhich is context aware. These policy mechanisms will take into account the risk, trust and potentialbenefits of sharing information. They will support negotiation in a decentralized environment in whichcentralized servers are not available and connectivity is intermittent. They will converge on the optimumpolicy and quickly transform that policy into the device specific configurations needed to minimize theworkload on the warfighter.


The Policy Based Security Management (PBSM) project is constructed around a layered policymodel, as shown below. This forms the backplane for integrating and demonstrating research results.


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The project tasks will address three broad areas: Development of policy specifications and technologies to refine high level policy goals into

configuration settings.

Development of technologies for conflict detection and resolution and negotiation of policiesrelating to different units in a community of interest (CoI).

Development of technologies to infer active policies in a system from the observation ofconfiguration settings.

The approach includes extensive collaboration with Project 6, Trust and Risk Management inDynamic Coalition Environments. In addition, collaborations with Project 2, Interoperability of WirelessNetworks and Systems, and Project 5, Energy Efficient Security Architectures and Infrastructures, willalso be pursued. Below is a short description of the projects relating to TA2, and how they need to interactand cooperate to meet military requirements.

Tasks 1, 2 and 3 form the core of the research in the first year. Task 1 will begin with the scenarioprovided by the UK MOD2 and refine it with input from the alliance. Clarifying the environment,challenges and requirements will highlight the research issues associated with developing a generalizableapproach for providing adaptive and dynamic policy management among various coalitions. Clear,scenario driven, functional requirements will be established that will guide the research, so that it ishighly relevant to the needs of the US and UK governments.

2 A Coalition Command and Control Mission Scenario and Vignettes to Support Coalition SituationAwareness Research and Experimentation, TTCP C3I - CSA White Paper No. 4 v0.2


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Task 1 develops the foundation for research on tailoring policy levels of abstraction and developingtechniques for specification, analysis, refinement, and negotiation in the appropriate operational context.It will be defined in terms of:

Concepts and tools for policy specification, editing, analysis and refinement, and negotiation atdifferent levels of abstraction.

Techniques and tools for policy transformation across different layers of abstraction.

Interfaces of the various components and relationships between them and with the tools.

This task establishes the environment for the algorithms, protocols and techniques which will follow.Properly defining the layers, the data within each layer, the functions performed by each layer and theinterfaces to the layers will provide a stable environment for development of algorithms and tools. It willalso provide a theoretical model which will encourage the development of automated tools during thetechnology transition phase.

Task 2 focuses on the concrete policy layer. For each of the different types of policies that need to beexpressed, it will identify the appropriate format, (e.g., goals, declarative specifications or utilityfunctions), and define appropriate notations and specification languages. It will define the formalrepresentation of the different types of policies, which enables the formal models of policy enforcementrequired for analysis.

Task 3 addresses the development of a distributed firewall model for a coalition of MANETs. Anintermediate subtask is a means of learning the changing topology. In collaboration with Tasks 1 and 2,distributed MANET firewalls developed in this task may be used as a concrete policy layer during yearone. A detailed examination of the policy analysis and refinement techniques will be performed in thecontext of these firewalls to study the properties of these techniques in a dynamic environment.

The tasks for the first year are described in detail below. The major participants in each task are listed,and the lead for each task is highlighted.

Task 1: Establish the theoretical foundation for research in policy abstraction models forsecurity

Reseachers: (Seraphin Calo, IBM; Jorge Lobo, IBM; Morris Sloman, Imperial College; KirkSchloegel, Honeywell )

Problem Statement: Before engaging in formal models and analysis we need to establish a commonground for the different components required to build a policy-based security management system(PBSMS). Policies can be described at many levels of abstraction, starting from natural languages andending in executable code. We are envisioning a system that will be able to handle security policyspecifications at different levels of abstraction depending on the circumstances and context. Managingrelatively few devices at the local platoon level with little or no support from central command is differentfrom managing policies at the battalion or division level. The kind of support required, the amount ofresources available and the type of coalitions and collaborations will be different, and will requiredifferent (but inter-related) abstraction models.

Solution Strategy: The military requirements for security will be clarified and refined based upon thealliance vision and specific scenarios. These scenarios of interest must be captured in enough detail thatfunctional requirements can be derived. Detailed scenario specification will be done at a global level inthe alliance to give a common focus for all projects. However, we need specific security requirements forTA2, so we will make use of existing scenario information to elaborate on security aspects relating to thefollowing aspects:


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Dismounted soldiers: The soldier has limited communications, processing and battery power. Theequipment carried by a soldier is at risk of being captured by the enemy.

Interacting vehicles (e.g., joining a convoy): These vehicles could be moving quickly; therefore,it is important that the system negotiate policy updates quickly. This scenario provides theopportunity to have a lead vehicle share results with other vehicles without having all vehiclesnegotiating with all other vehicles.

Interactions between cooperating forces: This requires information sharing by means of contextaware policies, taking into account risk, dynamic trust and the potential benefits.

The scenarios should consider both traditional warfare and anti-terrorist activities in order to highlightrelevant challenges.

Based upon these operational scenarios, requirements for policy based security management will bederived.

The aspects of the scenarios that are relevant to policy based security management will bedocumented and analyzed to determine operational (user) and functional (system) requirements.These requirements will be stated in broad terms suitable for research, as opposed to beingspecific to a given system. For example the security requirements relating to a body area networkof tens of nodes on a foot-soldier will be very different from those relating to thousands of nodesin a division.

These requirements will be interpreted with respect to the overall architectural specification,allowing us to identify the types of policies that need to be considered. We will also identify theimplications for the operational framework for policies, the elements of trust and risk that need tobe considered when specifying policies, and the types of dynamic coalitions where negotiation ofpolicies is needed to resolve conflicts.

The specific requirements for each of the processes necessary for policy specification anddeployment (e.g., number of nodes, constraints, interactions, etc.) will be determined. For policyauthoring and editing, various current research efforts will be examined (e.g., SPARCLE andMDBT toolkits at IBM). Their characteristics and capabilities will be analyzed within the contextof the relevant scenarios, and the necessary refinements and extensions will be identified. Forpolicy analysis, refinement and enforcement, current work at IBM (PMAC, Policy AnalysisToolkit), Imperial College (PonderART and SMC), and in the research literature (Rei, I2-D2) willbe examined. Similarly, for policy negotiation, efforts in GGF (WS-Agreement) and elsewherecan be studied to get an initial view of what capabilities are currently being considered.

A dialog with the government alliance members who have a solid understanding of coalitionoperations will be maintained throughout in order to make sure that we fully understand the problem, andthat we identify the policy requirements in the correct context.

An architectural framework will be developed to address the policy requirements. This frameworkcan be considered the backbone of the policy based security management project. The framework willevolve as the overall project evolves, and it will accommodate the various models that will be developedfor different aspects of policy based security management. We will start with a framework that buildsaround the current results and models developed by IBM and Imperial College for policy specification,refinement and ratification. Taking the policy requirements, we will identify the types of policies neededfor a simple scenario (e.g., a platoon) with hundreds of devices of a few types, as well as identifying whataspects of trust and risk need to be specified by these policies. We will then elaborate formal modelsrequired to support the management of policies, and identify suitable abstractions at this level to specifypolicies relating to the scenario. This will be both a top-down and bottom-up iterative approach torefining the architecture. In later years of the project we will investigate a more complex scenario with aone or two orders of magnitude greater number and types of devices to be handled, and more complexinteractions with coalition allies. This will be used to refine and revise the security management modelsand will elaborate on the more complex types of negotiation and conflict resolution needed. Trust and


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risk management has a direct impact on the management of security policies. Hence, the selection of theinitial scenario to drive the framework will be done in coordination with Project 6.

Output: The results of this effort will consist of white papers, research reports, and technical papersfor conference and journal submission. The types of documents envisioned include the following:

A white paper describing the scenarios in enough detail that the various efforts in the securityacross a system of systems technical area can extract the functional requirements that will driveresearch. This document may need to be revised and updated as more detailed scenarios emergefrom the global alliance scenario activity.

A document describing the requirements for policy-based security management with respect tothe scenarios. This document may need to be revised and updated as more detailed scenariosemerge from the global alliance scenario activity.

A document describing the security framework which contains the following information:

A classification of the layers of policy management and classes of policies driven by theestablished requirements. This is a combined output with Task 4.

A detailed architectural framework for the selected scenario that shows how policies at highlevels of abstraction get translated into one or more classes of concrete polices. Depending onthe advances in Task 3, firewalls may be used in the concrete policy layer.

A global architectural framework for the whole system will also be defined. Although with lessdetail than the output above. This global view will provide checks that we are not missingfundamental issues at the lower levels. The framework will consider refinement, ratification,trust and risk, negotiation and enforcement. We will describe:

o Components for policy negotiation and policy transformation across different layersof abstraction;

o Interfaces of the various components and relationships between them and with thetools; and,

o The different analysis and refinement methodologies to be used in each of thecomponents and tools.

Task 2: Policy Specification and FormalisationResearchers: ( Emil Lupu, Imperial College; Arosha Bandara, Imperial College; Seraphin Calo,

IBM; Jorge Lobo, IBM )

Problem Statement: Policies apply in coalition environments at multiple levels of abstraction, inmultiple functional areas and in multiple authority domains. Each independent organisation within acoalition will have its own set of policies and strategies whilst common policies will need to be agreed forthe coalition as a whole as well as for subsets of partners in the coalition. Policies will be specified foraccess control and security management, filtering information and adapting the security configuration ofnetwork elements, services and resources according to operational needs. High-level policies will bederived from military objectives and will need to be refined and elaborated in operational policies forindividual units and devices. In this context, there is a need for:

Concise notations and tool support for specifying policies relating to both different levels ofabstraction as well as different functional areas. For example the types of policies needed infirewalls in a fixed network will be rather different from the policy identifying who can benotified when a wireless sensor detects a nearby tank.

A formal representation of the policies in order to be able to apply conflict detection, policyanalysis and policy refinement. This will enable personnel to review the specifications and ensurethat they are consistent, can be implemented by the available equipment, and can achieve theoverall mission goals.


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Decentralised processes and algorithms for policy analysis and refinement that can be carried outlocally with different computational capabilities (e.g., at local command, in a vehicle or withhandheld devices) in order to cater for varying operational scenarios. We cannot assume that thecomponents where this analysis and refinement will take place will have access to centralizedpolicy services.

Solution Strategy: The requirements analysis in Task 1 will help us identify the different types ofpolicies that need to be expressed. During the first year we will focus on a subset of policy types and willdefine the notations to be used at different levels of abstraction, the operational semantics of the policies,and requirements for underlying security and communication mechanisms. This will be followed by agap-analysis identifying which properties of a policy specification need to be ascertained in givenoperational scenarios, and which techniques are more suitable for their analysis while considering thecapabilities of the devices to be used. We will then define initial versions of the formal representations forthese policies in order to enable formal analysis using an appropriate methodology such as model-checking, abductive inference or policy ratification. This will enable us to start working towards definingthe algorithms and techniques needed. Note that this work will also contribute towards an enhancement ofaccreditation procedures as it will enable characterization of permissible policy configurations andtherefore permit formal specification of the constraints relating to the use of both hardware and softwarecomponents in operational settings.

We will also start initial work towards identifying decentralized techniques for policy refinement, i.e.,the stepwise derivation of implementable policies from higher-level abstractions. Techniques are neededin order to ensure the correctness of the refinement process, ensure that the resulting policy specificationachieves the higher-level objectives and that the refinement is minimal or close to minimal. Specifyingand refining policy will lay the groundwork for policy conflict resolution and negotiation in year two, aswell as consideration of more complex scenarios.

Outputs An elaborated requirements specification identifying the different types of policies and their use.

This will be a joint deliverable with Task 1.

A coherent set of notations for a subset of the policy types needed as well as their formalrepresentation.

A detailed examination and initial definition of the decentralized policy analysis and refinementtechniques required.

Task 3: MANET Firewalls as a Fundamental Concrete Policy LayerResearchers: ( Steve Bellovin Columbia University, Hang Zhao and Maritza Johnson, Columbia

University, Jorge Lobo, IBM; Emil Lupu, Imperial College)

Problem Statement: Firewalls in traditional static networks provide one of the most basicmechanism to implement policy based security. Traditionally, firewall policies are specified in the contextof a network topology and the set of services from nodes within the network. This is valid for a staticenvironment, however, in a MANET the topology changes. This can cause the firewall policy to be out ofstep with the actual network. The task will study how firewalls can be extended to become effective in a


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coalition MANET environment. This task will provide a fundamental concrete policy layer to this projecton which ideas of policy refinement, conflict resolution, and negotiation can be applied and examined.

Solution Strategy: Static firewalls are good for simple, closed networks. Such networks, however,do not exist in highly dynamic MANET environments. Fully distributed firewalls, as described byBellovin in 1999, impose a considerable load on each end node. While full-fledged, desktop PCs couldmanage the cryptography without much trouble, the same cannot be said for small, energy-limited mobilenodes. Accordingly, it makes sense to take advantage of more centralized firewalls when possible.

Optimizing firewall policy to a changing topology requires secure knowledge of topology, as well ascommunication and negotiation of security policy. Nodes could start by listening to an authenticatedrouting protocol. The node would then compare its security policy with the security policy of anyauthorized firewalls it discovers. It would also use firewall analysis systems to verify that it is indeedprotected from possible enemies. Systems for static analysis of firewalls exist (see for example Fang[Mayer et al., 2000] or the argumentation logic work [Banadara et al, 006]). Naturally, in a dynamicenvironment, such analysis must be carried out continuously, in response to every relevant topologychange. In collaboration with Task 4, this task will develop definition languages and analysis strategies toaddress challenges posed by dynamic environments.

As individual nodes move around, they need to assess the authority of other nodes to act as firewallsfor them. This will depend on static measures, such as the current tactical organization, but also on thecurrent threat environment: a node that is in danger of capture should not be trusted, for obvious reasons.Less obviously, a rapidly moving node may be a poor choice, because it may drop out of thecommunication graph too soon. One solution is to broadcast movement intentions, but such informationis extremely sensitive. Thus, implementing a distributed firewall for MANETs faces multiple uniquechallenges that are absent from static networks with symmetric, homogeneous nodes.

The firewalls have their own role to play. As formerly adjacent networks separate, the firewalls mayneed to create cryptographic tunnels between them. This decision will depend on communication patternsbetween the networks.

Outputs: The main output of this task is the definition of a firewall model for a coalition of MANETs. An intermediate subtask is a means of learning the changing topology. The results of this subtask

will also be a contribution to Project 2, Interoperability of Wireless Network and Systems. In collaboration with Task 2, using MANET firewalls as a concrete instance, a detailed

examination and initial definition of the policy analysis and refinement techniques required willbe undertaken

Task 4: Policy Decisions based on Trust and RiskResearchers: (Jorge Lobo, IBM; Subash Shankar, CUNY; Dakshi Agrawal, IBM)

Problem Statement: The trust and risk management approach developed in Project 6 will provide thecapability to compute trust and risk metrics that capture operational context at various levels ofabstraction. It will be necessary to specify policies to make security decisions based on levels of trust orrisk. For example, the choice of particular encryption or authentication mechanisms may depend on therisk relating to the communication channel being used. What services or information is provided to anally within a coalition will depend on the degree of trust. Both trust and risk are not static properties butchange over time. Operational personnel need flexible tools which will allow them to make policydecisions and if necessary over-ride the normal policy settings when the benefits outweigh the risks. Forexample, a normal policy may prevent classified information being sent over a high-risk communicationlink, but in the operational situation where the normal secure links are down the use of the high-risk link


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may be required for very time-critical information transfer. The policy based security management musttake into consideration these trust and risk metrics when setting policies, but it must also provide foradaptation to current conditions and context, if necessary with human beings involved in the decisionloop. Thus, it is necessary that policy architectures and specifications developed in this project becompatible with the trust and risk models developed in Project 6. The goal of this task is to informdevelopment of models and specifications of policies undertaken in Project 4 with trust and risk modelsdeveloped in Project 6, and demonstrate the value of integration between policies, trust, and risk.

Solution Strategy: Trust and risk are not always easy to quantify so policies will interface with thetools and techniques being developed for specifying, measuring and updating trust and risk. Forexample, trust could be specified in a comparative fashion (low, medium, high) or as a numerical rangeindicating uncertainty in the value. We will investigate appropriate layers in the policy model where thetrust and risk metric (that will be under development in Task 3 of Project 6) should provide input as wellas the degree of integration between policy and trust specifications. This will enable dynamic policybased upon current assessments of trust and risk. We will investigate implementing trust and risk metricsas constraints within the policy specification language. Our initial work will be guided by a case-studyagreed upon by Project 4 and Project 6 teams, e.g., decision about whether to make a proposed networkconnection in a MANET, and if so, then how configuration of the proposed connection is affected by thetrust and risk input, and at what computational cost.

Output: The output of this task will take methods developed in Task 3 of Project 6 and integrate theminto the outputs of Task 1 and Task 2. It would provide a policy framework that dynamically takes trustand risk metrics explicitly into account, and a trust and risk management approach that is aware of theconsumption of trust and risk metrics into policy based security decisions.

Task 5: Policy Enforcement in dynamic environmentsResearchers: ( Tom Markham, Honeywell; Kenny Patterson, Royal Holloway, Project 5; Dakshi

Agrawal, Project 6 )

Problem Statement: The high level policies specified via the policy model must eventually beimplemented by mechanisms in the devices that permit or deny access to resources or informationtransfer, encrypt a message or authenticate a user. The mechanisms in a tactical MANET supporting acoalition are expected to be different from those in a static, wired IT infrastructure supporting a singleorganization. This task will identify the appropriate policy enforcement mechanisms and specify theirinteraction with the policy model. Examples of how the environment is expected to influence the securityinfrastructure mechanisms include: Low bandwidth, low energy: The security negotiation and configuration mechanisms must operate

over a low bandwidth, intermittent wireless link using minimal energy in order to conserve batterypower. This is in contrast to desktop and server machines with reliable high speed wired networkconnections and unlimited facility power.

Discovery and negotiation: The IT environment is relatively stable, whereas the MANETenvironment is very dynamic with nodes joining and leaving over timescales of seconds to minutes.Therefore, discovering new nodes and negotiating policies with them is a significant challenge. Thenegotiation is made more complex because in a tactical environment the unknown node may becontrolled by the enemy. Attempting to negotiate with it could reveal location information whichcould be used to direct enemy fire.

Localized negotiation: The IT environment generally provides centralized servers such as certificationauthorities which can be queried to validate credentials. The wireless, isolated MANET environmentwill require devices to perform localized negotiations in order to determine whether entities can betrusted without the benefit of access to a centralized server.


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Physical compromise and dynamic revocation: The policy for a wired corporate IT environment doesnot need to concern itself with physical compromise of servers. In contrast, the tactical MANETenvironment must provide mechanisms to detect the compromise of a node and rapidly update policyto isolate it and prevent interference or access to other entities in the MANET.

Solution Strategy: This task will be performed in collaboration with Project 5, Energy EfficientSecurity Architectures and Infrastructures. It will also work with Project 6, Trust and Risk Management inDynamic Coalition Environments, to identify how trust and risk can be used for policy-based selection ofsecurity mechanisms. The vision and requirements tasks will illuminate how the tactical, coalitionMANET environment influences the security mechanisms available, the task to be performed and theresources available for accomplishing the tasks. The team will work both top down and bottom up.

1. The policy team will work down from the policy layer of the model toward theimplementation/configuration layer. The policy team will analyze policies and recommendmechanisms which could be used at the implementation/configuration layer to enforce policy.

2. The Project 5 and 6 teams will work bottom up. They will identify:

a. Data from the devices which could be fed up to the policy model to support dynamicpolicy; and,

b. Mechanisms available in devices which could implement policy.

3. The team will then work with the outputs from 1 and 2 above to create genericimplementation/configuration layer interface descriptions which will allow development andvalidation of policy research without restricting the policy to a specific device implementation.The objective will be to define interfaces which are not specific to particular devices or tospecific security mechanisms.

Outputs: The output of this task will be the definitions of policy enforcement mechanisms and theirinterfaces to the policy model. These interfaces will support pushing policy down to configuremechanisms as well as reporting information up (new node discovered, topology change, compromisednode, etc.) to the policy model which will influence the implementation of policy. This will be a jointdeliverable with Project 5.


If the warfighters communicate the wrong information to the wrong person/device, it could cost themtheir lives. Likewise, if a warfighter is not able to communicate and share the right information with theright people, it could lead to loss of lives. The goal of the security policy project is to make it as easy aspossible to properly configure all of the devices in the network to provide the warfighter with highlysecure, highly available communications.

The refinement of the alliance vision and extraction of requirements will ensure that the remainingtasks are focused on the critical military security policy problems. The expected results of the first year ofresearch are that there will be a policy model and a set of policy specification mechanisms that provide aframework for resolving coalition policy and turning high level policy into configuration information formilitary equipment. The remaining tasks extend this basic policy model to address the dynamic MANETenvironment and the unique configurations and limitations of the military environment. These includedynamically building trust, using bandwidth efficiently, discovering changes in the MANET topology andconfiguring the devices via the security infrastructure interfaces.


The following collaborations and rotations have been identified for Project 4. Imperial visit to IBM Hawthorne 8-9 June 2006


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York visit to Imperial late July to discuss Project 6 collaboration TA2 meeting in New York Nov 6-8 2006. IBM, CUNY and Columbia (all in New York) to collaborate on Task 5 Possible longer term visits between IBM and Imperial during 2007 but details to be arranged Columbia visits to IBM Nov, December, and possible Research student placement during

summer 2007. A side ITA meeting during IEEE Workshop on Policies for Distributed Systems and Networks

in June 2007.


Management of security configurations in a static environment is a challenging task. Many of thesecurity breaches which occur are not due to a failure of the security mechanism itself but due to theimproper configuration of the existing security mechanisms. Security in a MANET environment requiresdynamic configuration of these security mechanisms. This is only practical through automated policymanagement systems like those being developed in this project.

Imperial College is involved in the Systems Engineering for Autonomous Systems DefenceTechnology Centre with a project on Self Managed Mobile Cells - the emphasis is on use of policies formanaging collaboration between groups of autonomous vehicles. We expect that some of the concepts forcollaboration between autonomous vehicles may be useful for collaboration between devices negotiatingsecurity policy in a distributed, dynamic environment.


Research Milestones


Q1Task 1-5

All tasks will focus on refining the alliance vision. Scenarios will bedocumented in a report and analyzed to extract the requirementsassociated with security policy (Honeywell lead).

Q2 Task 1A requirements report will be completed for Policy-based Security (IBMlead).

Q2 Task 1A first draft report of the security framework released for comments tothe project members. Sufficient details for the core layer for Task 2 toprogress (IBM lead).

Q2 Task 2Scenarios description. Selection of policy types on which we will focusduring the first year. Preliminary ideas on policy notations. Resultsprovided in a research brief (Imperial lead).

Q2 Task 4Identify challenges and potential points of integrating policies, trust, andrisk models in a research brief (IBM lead).


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Research Milestones


Q2 Task 5Relevant policy objectives for the computing and networking functionsare clearly identified in a research brief (Honeywell lead).

Q2 Task 3Design an experimental network for evaluation of policy framework forMANET firewalls (Columbia lead).

Q3 Task 1Re-evaluation of the security framework after comments and other taskshave the chance to work with the proposed framework. Delivered via areport update (IBM lead).

Q3 Task 2Report on policy notation and initial view of analysis requirements andformal representation of policies (Imperial lead).

Q3 Task 4Report identifying points of integration between policy, trust, and riskframeworks, initial analysis of such integration points (IBM lead).

Q3 Task 5

Mechanisms (protocols, algorithms, etc.) are identified and correlated tothe policy objectives. These are not final mechanisms but ratherfunctional descriptions (e.g. authenticate remote device) documented ina report (Honeywell lead).

Q4 Task 1Follow-up re-evaluation and plan for expansion to other layers orscenarios. Provided via a research brief (IBM lead).

Q4 Task 2Policy analysis techniques and initial views on policy refinement to besubmitted to a journal (Imperial lead).

Q4 Task 4A journal submission addressing analysis of integration between policy,trust and risk framework, models, and specifications (IBM/Columbialead).

Q4 Task 5Interfaces between the mechanism to be configured and the policymodel are identified. Delivered via a report (Honeywell lead).

Q4 Task 3Write draft paper with design of firewalls analysis tool and applicabilityof policies to dynamic firewalls. (IBM/Columbia lead)


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8. Project 5: Energy Efficient Security Architectures andInfrastructures

Project Champion: Kenny Paterson, RHUL



Kent D. Boklan, CUNY Dakshi Agrawal, IBM

Virgil Gligor, UMD John McDermid, University of York

Jinwoo Kim, CUNY Tom Markham, Honeywell

Andrew McDonald, RMR Chris Ketley, DSTL

Kenny Paterson, RHUL Richard Gopaul, ARL

Stephen Wolthusen, RHUL

Peter Wild, RHUL

Tal Rabin, IBM


The challenge of providing a security infrastructure for coalition formation and operations presentsmany complex problems, particularly in the MANET environments that will be seen in future militarynetworks. Such networks are intended to be self-organizing, self-discovering, rapidly changing intopology and devoid of dedicated infrastructural elements. Network elements are likely to be severelyresource-constrained (particularly in terms of bandwidth, memory and processing power) and have onlylimited bandwidth and intermittent connectivity. A degree of security pre-configuration will be possible,but the operational requirements to support dynamic coalition forming and secure communicationsbetween devices from different security domains poses great challenges to traditional approaches toachieving security.

In addition to the strict constraints on energy and bandwidth consumption, a unique set ofrequirements of secure, flexible taskforces is that of automated, wholesale selective distribution,revocation and review of access permissions. When a domain joins or leaves a coalition, security policy is(re)negotiated and a result of that negotiation is a set of permissions that must be granted (for joins) orrevoked (for leaves) to users of member domains. This is particularly challenging where taskforcemembership could be for a relatively short duration. Further, different members of the coalition may haveto establish trust relations with the joining domain on a dynamic basis, automatically, without the benefitof administrative intervention. This is particularly relevant in dynamic coalitions when not all members ofa domain join the coalition simultaneously. An extra challenge appears when the coalition, or a coalitiontask force, is created ad hoc without relying on a public-key infrastructure. In such cases, the trustinfrastructure, which forms the basis for all access control decisions to shared resources, must beestablished by evaluating trust evidence (e.g., certificates signed by coalition members and stored in acommon peer-to-peer storage area) dynamically. Dynamically established trust relations can be cachedand reused, possibly in different task forces of the same coalition.

Historically, either dedicated symmetric key hierarchies or public key infrastructures (PKIs) havebeen used to provide the underlying cryptographic key management in secure communication systems.However, recently alternatives to traditional PKI such as Identity-based Public-Key Cryptography (ID-PKC) and Certificateless Public-key Cryptography (CL-PKC) have emerged. These new alternatives


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promise smaller energy and bandwidth requirements, the capability for non-interactive key establishment,and the potential for better scalability of user registration and key management processes. Both the ID-PKC and CL-PKC concepts are relatively untested in practice, with commercialization of ID-PKCbeginning only very recently. Application of these approaches to the military environment and coalitionoperations remains almost virgin territory. The exploration of ID-PKC and CL-PKC techniques will becomplemented with research on how to use more established cryptographic techniques developed forother applications such as broadcast encryption, multicast authentication, key distribution centers,threshold cryptography in the MANET environments. While these cryptographic techniques weredeveloped with other goals in mind, we believe that by advancing their state-of-art, we can provideattractive infrastructure options in the MANET environments.

The background above gives motivation for the overarching objective of this research activity: thedevelopment and analysis of lightweight (efficient) and adaptive security infrastructures to facilitatesecure formation of and operations by dynamic coalitions in MANET environments. The proposedresearch will explore how to design and use evaluation metrics for trust evidence, with a focus onincorporating multiple types of evidence. It will investigate application of more established cryptographytechniques for MANET environments, and also investigate alternatives to traditional approaches tosecurity infrastructure made possible by ID-PKC and CL-PKC.


The technical work of the project will divide into three main tasks with a number of subtasks.

Task 1: Joint Taskforce Authorities and Permission Management SystemsResearchers: Virgil Gligor (UMD), Wolthusen(RHUL).

Objective: Determine the requirements for dynamic trust establishment among various domains ofcoalition task forces.

Approach: In particular, focus will be placed on how to design evaluation metrics for trust evidence,how to evaluate both positive and negative evidence (as might be required in revocation of trustrelations), and how to incorporate multiple types of trust evidence (e.g., taskforce member location,identity, configuration) that can be generated on the fly [1]. A further objective is that of being able todesign systems that tolerate false evidence being introduced in the evaluation system by a maliciousadversary. This is important because nodes of a taskforce network created in an ad-hoc manner could becaptured and/or controlled by a malicious adversary. Such nodes could generate false evidence (e.g., falsenode location certificates, false node configuration certificates, false identity certificates) that would leadto false trust relations to be formed and used thereby causing improper permission distribution, reviewand revocation decisions. It is envisaged that permission distribution, as well as review and revocationpolicies for different principals, will be addressed in subsequent years of this project.

Outputs: Research paper explaining the relationship between access control and trust establishment,dynamic trust establishment, trust evidence evaluation, role of evaluation metrics, and logics for evidenceevaluation.

Task 2: Trust Authority mechanisms for MANETsResearchers: Boklan (CUNY), McDonald (RMR), Paterson (RHUL), Wild (RHUL), PDRA (RHUL),

Rabin (IBM), Government researchers.

Objective: To investigate new security mechanisms that do not require certificates in MANETs.Approach: There has been some early work in the literature that applies mechanisms like ID-PKC to

ad-hoc and sensor networks, (including papers by Carman [2], Khalili et al. [3] and Matt [4]). However,the strengths and shortcomings of such a mechanisms have not been completely studies, and several open


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problems and issues remain unaddressed. An example of such a problem is the usefulness (for MANETenvironments) of the ability of ID-PKC and CL-PKC to allow altered message flows and non-interactivityin public-key cryptography. Another open problem is the support of a distributed trust authority in aMANET environment.

We will examine how both the functionality of a TA and revocation processes can be distributed inMANETs. The motivation for studying this problem is that a distributed approach will assist in counteringthe problem of TA and node compromise that might be expected in a military environment. The mainfunctions of a TA are to authenticate entities, and then create and securely distribute private keys toentities. In some settings, particularly those where identifiers are dynamic, these functions may need to becarried out relatively often and across the MANET network itself. So finding efficient mechanisms willbe important. However, TAs may be compromised, and entities will be uncertain as to the exactcompromise status of TAs. Therefore, we envisage an entity collecting TA status information from anumber of TAs and/or other entities, then forming an opinion as to which TAs can be trusted (and to whatextent), before engaging in distributed key management with the relevant subset of trusted TAs. Similarly,entities providing revocation information (whether they be TAs, revocation authorities or simply othernetwork nodes) may also be untrustworthy. To handle these uncertainties, we plan to develop and studythe feasibility in MANET environments of a distributed approach to revocation, with multiple sources ofinformation concerning the status of TAs, public keys and identifiers being available. We anticipate anentity resolving this information before making a decision on whether or not to use a particular public keyor identifier. This part of Task 2 will link naturally to the work in Task 1 of this project: the evidentialapproach to generating trust developed in Task 1 will provide one possible mechanism for evaluatinginformation when making decisions on use.

Outputs: Research examining how the functionality of a TA can be distributed in MANETs in theface of explicit compromises of TAs. Technical report examining performance of different revocationstrategies in MANET environments with distributed ID-PKC and CL-PKC TAs.

Task 3: Key Management to Support Dynamic Hierarchies and Coalition FormationResearchers: Boklan (CUNY), McDonald (RMR), Paterson (RHUL), Wild (RHUL), PDRA (RHUL),

Rabin (IBM)

This task will investigate how current ID-PKC/CL-PKC key management techniques can be extendedto support dynamic hierarchies as well as coalition forming and dissolution in the presence of multipleroots of trust. Here the main challenge is to find ways of allowing flexible inter-operation of keys,identities and other security parameters for entities having different roots of trust, and finding ways ofthen limiting this inter-operation once a coalition has been disbanded. As part of this work, we will studythe “interface” between the proposed cryptographic techniques and their applications. In the context ofID-PKC/CL-PKC, one of the main aspects of this issue is the choice of identifier/identity. In particular,we will examine what identifiers (users, roles, network addresses, etc) can be used, and the properties ofthose identifiers, e.g. whether it may be appropriate to use different identifiers for different tasks or atdifferent times. One approach to limiting inter-operation after disbanding of a coalition that we willexamine is revocation of the relevant identifiers/keys. The literature contains a number of differentproposals for handling revocation in ID-PKC systems. We will evaluate the suitability of these differentmethods in MANET environments. Our evaluation will be informed by the outputs of Task 1.

We will exploit the synergies and commonalities between existing schemes for key management andkey distribution in sensor networks (e.g. [5,6,7]), and schemes for broadcast encryption and multicastauthentication (see [8] for a survey) to explore possible applications of tools from the latter field toMANET security. We will research solutions based on a purely combinatorial approach, but also onalgebraic techniques such as secret sharing, threshold cryptography, efficient multiparty computations,and others. We will examine the possibility that in a setting where a low energy system can be aided by a


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high energy system computing tasks will be transferred from the low energy system to the high energyone. This will need to be done under the consideration of trade-offs between local computations andtransmission of data. We will stress the provable security of our solutions while striving for optimalefficiency and ease of implementation and deployment.

Output for Task 3: Research papers on (i) how current ID-PKC/CL-PKC key managementtechniques can be extended to support dynamic hierarchies as well as coalition forming; and (ii) new andimproved symmetric key management for MANETs that include novel approaches and detailedperformance analysis of our solutions.


In this project, we will research security architectures and infrastructures for dynamic coalitionenvironments. In particular, our research will be directed towards finding security infrastructures and keymanagement techniques that support vital security functions such as authentication and establishment ofsecure communications suited to dynamic, resource-constrained, mobile ad hoc networks (MANETs) ofthe type envisaged in future tactical military networking environments. Such techniques will theunderpinning security infrastructure that is essential in enabling the secure formation of dynamic

coalitions. Collaborations and Staff Rotations

Intra-project collaboration will take place mostly using electronic means, with face-to-face meetingsbetween partners when possible. International conferences will be an important venue for meetings wherecollaborative work can be pursued. We have identified Usenix, ACM-CCS, NDSS, ESORICS and IEEE-SP as being potential venues for this kind of activity. We also plan a 2-day workshop for all Project 5 staffto be held in early 2007 at CUNY.

Inter-project collaboration will take place mostly with Project 4 (Policy-based security management)and Projects 1-3 (in the Network Theory Technical Area). The Security Technical Area project staff is inthe early stages of planning a 2-day workshop to be held in October/November in the US. This willrepresent an important opportunity to build collaborative activities both within and across the Securityarea projects. Robert Hancock (RMR) has been identified as a key collaborator within the NetworkTheory technical area.

RHUL and the University of Maryland have active academic visitor programmes and welcome shortand medium-term visitors from ITA partners. Details of exchanges will develop as staff are hired into theproject and opportunities for rotations arise.

IBM has an internship programme suited to advanced level masters and graduate students. We willexplore the use of this mechanism to place students from academic partners on internships with IBM towork on ITA-related research.


The work under Task 1 has a different scope than work under CTA at University of Maryland.The work under CTA does not address dynamic coalition and task forces and refers to security aspects

of networks that operate in hostile environments. Specifically the CTA work refers to emergent propertiesand protocols for network security (e.g., sensor network security) under new adversarial attacks.

The work under tasks 2 and 3 is aimed at examining the extent to which identity-based andcertificateless infrastructures can be used to meet the stringent security requirements of MANETenvironments. These design architectures are relatively new, and we are not aware of extensive pre-existing research in this area.


Research Milestones



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Q1 Task 1 A paper surveying the existing research on trust evidence and evaluationof metrics for trust establishment in dynamic coalitions and taskforces. Asummary of the specific research problems currently not addressed.Responsible: UMD.

Q1 Task 2 Work surveying the existing (limited) research in the application of ID-PKC in ad hoc and sensor networks, identifying its strengths andshortcomings, as well as open problems and issues currently notaddressed. Responsible: RHUL/CUNY/IBM.

Q2 Task 1 Paper explaining the relationship between access control and trustestablishment, dynamic trust establishment, trust evidence evaluation,role of evaluation metrics, and logics for evidence evaluation.Responsible: UMD.

Q2 Task 3 Paper examining how current ID-PKC/CL-PKC key managementtechniques can be extended to support dynamic hierarchies as well ascoalition forming and dissolution in the presence of multiple roots oftrust. Responsible: RHUL/CUNY.

Q3 Task 1 Draft final report on metrics for dynamic trust establishment in dynamiccoalitions and task forces and on dynamic access control.Responsible:UMD.

Q3 Task 2 Research brief examining how the functionality of a TA can bedistributed in MANETs in the face of explicit compromises of TAs.New and improved symmetric key management for MANETs.Responsible: RHUL/CUNY/IBM.

Q4 Task 1 Final report and submission of research paper for peer review to leadingconference/workshop on network security. Responsible: UMD.

Q4 Task 3 Technical report examining performance of different revocationstrategies in MANET environments with distributed ID-PKC and CL-PKC TAs. Performance analysis of traditional cryptographic techniquesin MANET environments. Responsible: RHUL/CUNY/IBM.

8.7. References:

[1] L. Eschenauer, V.D. Gligor and J. Baras, “On Trust Establishment in Mobile Ad-hoc Networks,” Proc.of the International Workshop on Security Protocols, Cambridge, U.K., April 2002, in LNCS Vol. 2845,Springer Verlag, 2003.[2] D.W. Carman, ``New Directions in Sensor Network Key Management,’’ International Journal ofDistributed Sensor Networks, 1: 1–13, 2004.

0In n4Scc9ei.0snT5s o2a Dry2 l8Nios0ter3r taiwbnuodtr eFkd rK aSneecyni sM o3ra2 nN5a eCgtewhmeosertknnstut StreetPhiladelphiaPA191061550-13291550-1477

[3] A. Khalili, J. Katz and W.A. Arbaugh, “Toward Secure Key Distribution in Truly Ad Hoc Networks,”Proc. SAINT 2003, IEEE Computer Society Press, 2003.[4] B.J. Matt, “Toward Hierarchical Identity-based Cryptography for Tactical Networks”, Proc. Milcomm2004.[5] L.Eschenauer and V.Gligor. A Key-Management Scheme for Distributed Sensor Networks. Proc. of

the 9th ACM Conference on Computer and Communication Security, Washington D.C., November 2002.[6] V.Gligor, B.Parno and A.Perrig Distributed Detection of Node Replication Attacks in SensorNetworks. IEEE Symposium on Security and Privacy 2005.[7] J.McCune, E.Shi, A.Perrig and M.K. Reiter. Detection of Denial-of-Message Attacks on SensorNetwork Broadcasts. IEEE Symposium on Security and Privacy 2005.[8] R.Gennaro. Cryptographic Algorithms for Multimedia Traffic. Springer Lecture Notes in ComputerScience vol.2946


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9. Project 6: Trust and Risk Management in Dynamic CoalitionEnvironments

Project Champion: John McDermid, University of York



Dakshi Agrawal, IBM

Howard Chivers, Cranfield University3 John Melrose, DSTL

John Clark, University of York Natalie Ivanic, ARL

Trevor John, SEA

John McDermid, University of York

Dawn Song, CMU

RA, University of York (TBA)

Phd Student, University of York (TBA)


Security is intrinsically concerned with risk; however in current practice security is managed in termsof “hard rules”, governed by the accreditation process which do not allow for the dynamic assessment ofrisk. There is growing acceptance that the current security mechanisms are not appropriate to futurenetwork systems; indeed the issue has been known for some time [1], but it is only recently that theproblem has begun to be addressed in research programmes. This cannot be achieved just by modifyingthe existing approaches, and role of this project is to provide the innovation, e.g. producing good modelsof trust, which allows a migration towards dynamic risk assessment in coalition MANETs. This is aradical innovation which we believe is essential to deliver the expected operational benefits of NEC andNCW. This innovation requires explicit run-time models of trust, and benefit, for example to makedecisions to allow operations to proceed where the benefit outweighs the risk. Thus the project isconcerned with developing models of risk and trust, and linking to a wider decision framework that mustalso include benefit, in order to dynamically inform security related decision making. For brevity, we usethe terms “risk” and “risk framework” to encompass all risk and trust issues and their coupling to adecision framework, which must include operational benefit.

The current state-of-the-art in risk management is best thought of in terms of accreditation practice. Itis common for system accreditation frameworks to include a matrix of risks, indexed by the highestclassified document and lowest cleared user. This is sometimes extended directly to assurance levels, butis essentially a risk measure. This matrix enumerates the risks resulting from a limited range of inputparameters, using the same generic basis for risk: impact and outcome. Classification is defined in termsof impact, and user clearance is effectively a measure of the outcome, since it defines populations of usersfor the system. This approach is static, and tends to be conservative being based on worst case risk [2].

3 Prof. Chivers was at York when the bid was initiated. Prof. Chivers’ involvement in TITANS will be managedvia York.



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In order for the risk calculation to be carried out dynamically, it is necessary to obtain impact andoutcome estimates separately, depending upon circumstances. The dynamic calculation carried out in theproposed risk management framework is therefore comparable to an accreditation matrix, except that theprocess has been moved to the dynamic operational environment, and is able to take account of a widerrange of circumstances, including the potential for active trust management, and the ability to inform thepolicy and command decision processes.

In research terms, the vast majority of the publicly available literature is concerned with the“traditional” model of security and its adaptation to computer systems; this literature includes the classicalworks by Bell and La Padula. Very little has been published on the risk-based approach to security [2].

Part of the project will also consider unification of approaches to safety and security. Safety andsecurity are both risk-based – albeit concerned with impact on people (and material) and on information,respectively. However in a networked environment breaches of security, e.g. spoofing target data, cancompromise safety. The project will carry out preliminary studies of the possibility of using a commonrisk-based approach to managing safety and security, building on existing research, e.g. [3], particularlywork on using policies to manage safety risk dynamically, e.g. [4], as this may give an opportunity ofunifying the approaches to safety and security.


The project will explore how to manage security-related risk, evaluated dynamically, i.e. as thesystem is running, and how to use the results of the risk evaluation to inform decision making and systemmanagement. The approach will make use of risk-based policies, developed in conjunction with Project 4,and implemented and enforced via a widely applicable architectural approach.

The project will consists of four tasks. Task 6.1 is concerned with the theoretical foundation ofdynamic risk management, including a layered model that supports risk decisions at multiple levels ofabstracts, approaches to incorporate operational context in risk-based techniques, and methods forcalculating and managing risk in practice.

Task 6.2 is concerned with obtaining extra information about the risk environment, including theextent that it is possible to estimate the level of attack present in a network (or perhaps, the quality ofprotection provided by a potential collaborator) by extending and adapting existing research into intrusiondetection.

One potential problem with complex systems research is that it is hard to tell whether a proposedapproach would succeeded in practical environments. As befits a strategic research programme, wepropose an early task, 6.3, to investigate how rigorous evaluation of approaches proposed for riskmanagement might be conducted and what the issues involved are. Thus, part of our strategic first year’swork investigates methodological concerns. This task and its output report have potential applicabilityand relevance to many ITA tasks.

Finally, Task 6.4 will investigate the ability to address safety and security in a unified framework,with the aim of informing the development of the first BPP.

The initial year’s planned tasks are about finding out what the problems in the field are (or will/mightbe) and engaging in fundamental thinking about the problem and the way(s) forward. The tasks seek toexplore issues, define principles to guide proposed solutions, and investigate extensions to existing work.The results are largely technical reports geared to project needs. We adopt a strategic view of the researchprogramme and offer to address methodological concerns such as evaluation. It is not our intention todesign and implement any particular solution to evaluate at this stage; rather we believe that fundamentalthinking about the research context, options and methodology should drive the research in first year.

Some difficult issues face us in establishing the overall scope of the research, for example: How doesrisk management change when real-time constraints are imposed? What is the role for automateddecision-making and when/how are humans involved? What are the principles by which tradeoffs anddifficult comparison are made? What scientific and engineering foundational work must be carried out tomake dynamic risk management a credible possibility?


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Some research issues overlap with other groups, for example: the policy framework for risk-baseddecisions (project 4), the need to contrast risk with benefit in a command decision framework (commandtransformation in project 11), and the availability of primitive protocols for trust management (project 5).We will develop familiarity with the work of related projects, and identify the interface between researchcarried out by the dynamic risk-management team and other projects.

Task 6.1: Theoretical Foundation for Dynamic Risk ManagementResearchers: Agrawal, IBM; Chivers, Cranfield; Clark, York

Objective: Establish a theoretical foundation for dynamic risk management.

Approach: A theoretical foundation for dynamic risk management will include (at least) three keyaspects to the risk management problem: a layered model that supports risk decisions and trustmanagement at different levels of abstraction, inclusion of operational context in risk and trustmanagement techniques, and methods for calculating and managing risk in practice. This work willconsider aspirations for dynamic security management in various time frames (probably 5, 10 and 20years) and the technical characteristics of likely systems (number of nodes, likely geographical dispersalin a MANET, resource constraints, etc.) to inform the development of the theoretical foundation.

The architectural framework will consider risk calculation and management frameworks, and alsotrust management – mechanisms to actively manage the risk profile of dynamic connections. A layeredmodel of trust and risk management will be developed to reason about risk at various level of abstraction.The higher layers in this model will describe abstract risks (e.g. “an attacker obtains a sensitive documentvia a weak host”), while lower layers will provide more specific risks (e.g. “a compromised C2 node Ncould expose location of troops). In this task, we will determine possible layers, the languages ofdiscourse within those layers and the relationships between the layers. As above, we are aware of the needto ensure that any proposed trust and risk management framework must be compatible with the policyframework, also developed as part of TA2. Accordingly, we will revisit our position with respect to thetwo other projects in TA2 and work towards producing a unified architectural approach.

Our previous work on Security Design Analysis [2] shows that it is possible to develop a securityanalysis framework in which security concerns, risks, and mitigating mechanisms are superimposed onthe high-level design of a complex system to allow a system analyst to reason about the effect of changesto security or functionality. It can provide risk-related metrics to guide system implementation at thedesign time. In this task, we will extend this work to include the operational context and explore howrisk-related metrics can be derived at the runtime that allow informed security trade-offs to be madedynamically as opposed to at the system design time.

Risk calculation or estimation methods are needed to quantify outcomes in uncertain operationalscenarios, a range of deterministic, probabilistic and simulation approaches will be considered for furtherstudy, taking account of their likely performance and practicability in the future operational environment.

Output: Report summarizing the requirements for dynamic security management and the operationalcontext sufficient to be able to define “validation tests” for the techniques and methods developed inProject 6, and more widely in TA2. This task is targeted for late September. A sequence of short researchbriefs, each documenting candidate approaches to and analysis of various aspects of dynamic riskmanagement; at present three are planned related to risk and trust layered model, dynamic operationalcontext, and risk estimation and management. The three briefs will be consolidated into a final technicalreport.

Task 6.2: MANET Hostility QuantificationResearchers: Chivers, Cranfield; Clark, York; Song, CMU


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Objective: Establish possible approaches to determining the background level of network attackspresent in a MANET, or perhaps equivalently, the degree that a MANET is protected, and identifypromising solutions for further work.

Approach: This task will build on existing research on intrusion detection systems (IDS); however,unlike IDS systems where the objective is to identify incoming attacks, and perhaps to automaticallydefend a system, the objective of this work is to estimate how hostile a network environment is, with theaim of providing objective information to assist dynamic risk management. Another significant differencefrom the existing research on IDS is that most of the past work has focused on static networkinfrastructure. In contrast, the focus of this work would be on MANET.

There are two main aspects to the problem: measurement systems and their deployment context. Themeasurement options will be developed from existing IDS research, and include both the means ofmaking attack measurements and also the theoretical basis for value: what can be deduced frommeasurements on an otherwise unknown network. Deployment options are complicated because of thecollaborative ad-hoc network scenario in which such systems may be used: for example, if an IDS-likesystem is deployed as a trusted process on a collaborator’s network (in order to provide assurance aboutthe environment) what are the consequences for the collaborator in terms of information transfer or othersecurity risks, and what options are available to make such a deployment acceptable to both parties?

Output: A research report, documenting candidate approaches to the measurement of the degree ofhostility of a collaborating MANET, and related contextual and security problems in deployment of sucha capability. As a first step, we will investigate how to apply experiences from intrusion detection in wirednetwork to our problem setting, and also explore what new questions need to be addressed and what newtechniques need to be developed. A brief early report describing key research questions, and outlinestrategy, will also be produced to inform task 6.2.

Task 6.3: Methodological Aspects of Trust and Risk ManagementResearchers: Agrawal, IBM; Benjamin, Dstl; Cirincione, ARL; Chivers, Cranfield; Clark, York;

McDermid, York; Song, CMU

Objective: Investigate possible procedures and methods to evaluate trust and risk managementapproaches.

Approach: Most academic research work is technology focused; little thought is given towardsmethodological concerns. We propose to address the issue of evaluation of dynamic risk managementearly in the project. It is hoped that as well as providing guidance for the evaluation of proposedapproaches, the task will provide guidance for rigorous research in other technical areas too. Forexample, examination of the role of simulation in evaluation of complex dynamic systems should findapplication elsewhere.

The task will address this issue from the perspective of how approaches can be validated, or shown tobe invalid, and how sufficient confidence could be obtained in order to warrant starting to use themethods developed in the project in a technology transition activity. The task will address both the riskassessment mechanisms to be defined in the Project, and the architectures necessary to support theapproach to dynamic risk management, e.g. figures of merit to assess proposed architectures.

Output: A technical report outlining the methodological issues in evaluation of dynamic riskmanagement technologies, and making recommendations on how evaluations can be used in assessingand refining the research results.

Task 6.4: Safety and SecurityResearchers: Benjamin, Dstl; Cirincione, ARL; John, SEA; Ketley, Dstl; McDermid, York


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Objective: Assess feasibility of unified approach to safety and security, including survivability.Approach: Work has already been conducted considering the relationship between safety and

security, e.g. [3]. Perhaps most pertinent is work on using policy models to manage safety in a system ofsystems environment, representative of NEC/NCW, e.g. [4]. Research has also been carried out on policy-based approaches to survivability, e.g. [5], considering how to preserve key properties, e.g. safety andsecurity, under network attack and failures. The task will review the existing work on safety policymodels, survivability and the ongoing work in the rest of the TA in order to determine whether or not it isfeasible and useful to develop a unified approach to dynamic safety and security risk management. Thetask will investigate the use of policy-based approaches to safety and security risk management, andpropose a strategy for unification of work in this area, if this seems feasible.

Outputs: Report on possibilities of producing integrated risk and policy-based approaches fordynamic risk management, with recommendations for the BPP.


It is generally recognized in the UK and the USA that a radical change in the approach to security andsecurity management will be needed in order to realize the potential benefits of NEC and NCW. TA2 isconcerned with a range of related issues in network security. Project 6 is considering a radical change tosecurity management which, if successful, will enable security to be managed much more dynamicallyand flexibly than is currently possible, taking into account the assessed risk during actual operations. It isanticipated that this, along with the innovations in other parts of TA2, will enable security mechanisms tobecome a key success factor in future military operations, rather than a constraint on operations, which ishow they are typically perceived at present.


In the initial three months of this task, McDermid will take the lead, organizing visits to the keygovernment members of the Alliance, but relying on them to broker meetings with other MoD and USArmy stakeholders, e.g. Hillary Sillito the leader of the MoD’s Integration Authority and staff in DGInfo.A combination of informal information gathering and structured elicitation of aspirations for riskmanagement techniques, review of the literature, and discussions both within the project and with otherstakeholders will be used to further guide the research work of the team.

At this stage, we are planning a series of visits and organizing a TA-2 wide workshop on Nov. 6-8,2006 to facilitate collaboration. Staff rotations will take place during the IPP period (e.g. throughplacement of summer students in IBM internship program), though concrete plans have been deferred tillthe TA-2 workshop. At the time of writing, the following visits have been agreed in principle and are atvarious stages of planning:

Visits by UK staff to ARL and IBM Yorktown – mid August; Visits by UK staff to UK government departments, probably including CESG, CSIA, DGInfo, DG

S&S and the IA, during summer 2006. Meeting between Projects 4 and 6 (probably London, July). TA2 workshop – 6-8 November 2006, in the New York area.Interaction with other TAs will be planned after the workshop in /November.There are no formal collaborations with a CTA or a DTC, at this stage.


There is an enormous range of work on security in both governments and in industry. The work inindustry tends to focus on business needs, e.g. work on role-based access control (RBAC), for supportingmodern organizations. Military work has moved away from the classical focus on cryptography toconsider security more broadly, including operational support for management of risk. This latter area of


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research is pertinent to Project 6. It is quite possible that there is relevant work under way, e.g. in CESG,of which the project team are unaware. One of the aims of Task 6.1 is to gather knowledge about suchresearch, to ensure that TITANS adds value to the activities already under way in government.


Research Milestones


Q1 Task 6.1 An initial survey of the research scope, sufficient to identify areasof interaction with other project teams. (York lead).

Q2 Task 6.2 Research brief 1: Candidate theoretical foundations. (York lead)

Q2 Task 6.3 A technical report outlining the methodological issues in evaluationof dynamic risk management technologies, and makingrecommendations on how evaluations can be used in assessing andrefining the research results. (York lead).

Q2 Task 6.2 Report summarizing the overall task 6.2 research strategy andoptions (CMU lead)

Q3 Task 6.1 Research brief 2: Candidate risk estimation and managementapproaches. (York lead)

Q3 Task 6.4 Report summarizing the possibilities of producing integrated riskand policy-based approaches for dynamic risk management, withrecommendations for the BPP. (York lead)

Q4 Task 6.1 Research brief 3: Candidate architectures for risk and trustmanagement, taking account of the work of other projects. A technicalreport that consolidates the three task 6.1 research briefs. (York lead).

Q4 Task 6.2 Report summarizing the selected approaches to the measurement ofthe degree of hostility of a collaborating mobile ad-hoc network, andrelated contextual and security problems in deployment of such acapability. (CMU lead)

9.7. References:

[1] “Redefining Security—A Report to the Secretary of Defense and the Director of CentralIntelligence,” Joint Security Commission, Feb 1994; http://www.fas.org/sgp/library/jsc/.[2] H. Chivers, Security Design Analysis, PhD Thesis, University of York, 2006.[3] SafSec, http://www.safsec.com/ (there is a range of reports at this site).[4] R. Alexander, M. Hall-May, and T. Kelly, “Towards Using Simulation to Evaluate Safety Policy forSystems of Systems,” Proc. of the Second International Workshop on Safety and Security in Multi-AgentSystems, 2005.[5] Varner, Philip E. Policy Specification for Non-Local Fault Tolerance in Large Distributed InformationSystems, M.S. Thesis, University of Virginia, May 2003.

http://www.safsec.com/


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10. Project 7: Quality of Information in Sensor Data

Project Champion: Dr. Vic Thomas, Honeywell Labs



Dr. Chatschik Bisdikian, IBM-US

Prof. Tom LaPorta, Penn State University

Dr. Parviz Kermani, IBM-US Prof. Boleslaw Szymanski, RPI

Dr. Mark Nuttall, IBM-UK Tien Pham, ARL

Prof. Erol Gelenbe, Imperial TBA/RC Jitu Patel, Dstl

Prof. Ping Ji, CUNY

Prof. Mani Srivastava, UCLA

Prof. Deborah Estrin, UCLA

Dr. Yunjung Yi, Honeywell Labs


Objective: The objective of this project is to develop a framework that facilitates the description,analysis and estimation of the Quality of Information (QoI) delivered by a sensor network. In the contextof this project, QoI represents a measure of confidence that can be placed on information delivered todecision makers.

Innovations: (1) Formalization of the concept of QoI for sensor networks, (2) A QoI framework thatis amenable to both human and machine reasoning, (3) A QoI model that supports both physics-basedsensors and non-physics based sensors (e.g., HUMINT).

Research Issues to be Addressed: In its first year the project will focus on: (1) Developing an initialmodel for QoI, (2) Investigating the relationship between QoI and routing schemes in sensor networks,and (3) Understanding how sensor characteristics and calibration affect QoI.

State-of-the-Art: QoI research to date has been limited to narrow domains such as databases and websearches. This project will develop a QoI framework that applies to sensor network in general. Thisproject is novel because: (1) The QoI framework must accommodate sensor networks that incorporate awide variety of sensors and data fusion algorithms; (2) the framework must account not only for thecomplexities of sensor networks such as energy management schemes, routing schemes, node localizationschemes, time synchronization schemes and security schemes, but also the added challenges brought bytheir military contexts including network volatility, bandwidth limitations, unpredictable/hostileoperational environments, etc; and (3) it will expose the QoI trade-offs between sensors (data sources),processing elements (data fusion elements), network operational conditions, and so on.


The project has been structured around defining QoI in the context of sensor networks, sensornetworking specific issues that affect the QoI delivered by the network, and developing a framework thatrelates all of these together in a manner that facilitates QoI estimation for a given sensor network. To thisend, the following six tasks have been defined:


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QoI Definition and Transformation. This task will develop models for the description andspecification of QoI. It will also characterize the transformation of QoI by sensor data fusionprocesses.

QoI and Sensor Network Services. This task will investigate the relationship between QoI and sensornetwork services such as routing, energy management, time synchronization and node localization.

Sensor Characteristics and Calibration. This task will study how factors that affect sensorperformance—such as sensor resolution, calibration drifts and environmental effects—affect the QoIdelivered by the sensor network.

Sensor Trustworthiness. This task will focus on the detection of malicious attacks on the sensornetwork intended to degrade the QoI delivered by the network.

QoI Analytical Framework. This task will develop an analytical framework that enables sensornetwork synthesis (determining an optimal sensor network architecture given QoI requirements) andsensor network analysis (determining the QoI that a given sensor network is capable of providing).

Experimental Validation. The theories developed by this project will be validated using experimentaltestbeds and realistic scenarios.

Year 1 of this project will focus on Tasks 1 through 3 listed above. Each of these three tasks isdescribed in detail in the remainder of this section.

Task 1: QoI Definition and TransformationResearchers: Chatschik Bisdikian, IBM-US; Erol Gelenbe, Imperial, Vic Thomas, Honeywell, with

collaboration from entire Team

One key objective in this task is to facilitate the dissemination of information of the highest possiblequality to the “consumers” that need it, whenever it is needed, in the presence of highly unpredictable,heterogeneous, ad-hoc summoned resources (including processing-, energy-, and bandwidth-challengedsensors, communication links and data processors) that operate in hostile environments. To achieve this,we will explore the use of a metadata (or, data model) framework that captures the essential elements ofsensors, data processing (fusion) elements, and the volatile networks connecting them. The metadataframework will formalize the information that that will be needed by any process (as identified originallywithin TA3 tasks) that will dynamically tie sensors and fusion elements to provide a desired level of QoIto information consumers. Past research on this topic has been focused on “well-behaved” systems, wheremost of the information uncertainty is the result of the end-devices’ ability to accurately measure sensedphenomena ([1-6]). In our study, we will consider additional causes of sensor information uncertainty,e.g., component (sensors, processors) volatility, ad hoc operations, communications, processing, andpower limitations, etc. Specifically, we will start with a systematic literature survey of past research withemphasis the aforementioned causes of sensor information uncertainty and the breadth and depth of anymetadata frameworks that are pertinent to this task. Then we will work on building an initial metadataframework. The initial framework will be used to collect feedback from project collaborators to help usstrengthen its capabilities and identify aspects of it that will need further exploration. After that, ourintention is to author and submit for publication technical paper(s) describing our initial framework. Thiswill provide us with additional means to collect feedback on our work from the technical community atlarge that we can use to further advance our research on this topic.

Another key objective of this task is to study the impact on QoI that errors occurring during thesensor data collection (i.e., sensoring), data transmission, data fusion, and data processing and integration.This research is motivated by the limited work done for intellectually repairing “dirty data” (3) andinferring problematic hardware and software components (4) in a sensor network. Specifically, we willfocus on two QoI-impacting aspects: (1) data cleansing: as information about a single event of interestcan usually be reflected in data from multiple sources, some of the collected information can beinconsistent with others due to data quality issues. The redundancy in the multiple information channelscan be quite useful as each data source can serve as an error correction code for others. We will work onmodels for data collection and distribution, and study methods to identify and correct erroneous data


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among inconsistent observations. The outcomes of data cleansing are “error-corrected” information and afidelity measure of the information that would be useful for decision making. (2) QoI tomography: toidentify the “lossy” components (e.g., a lossy transmission channel, a malfunctioned data fusion node,etc.) where the quality of information is compromised. This is in the same vein of the networktomography study in which lossy internal data components are inferred from end-to-end measurements(5). The result of the QoI tomography shall be incorporated into the data model for data cleansing wheredata that traverse through the “lossy” component would associate a relative low fidelity level in futureprocessing.

Task 2: Relationship between QoI and Sensor Network ServicesResearchers: Y. Yi and V. Thomas, Honeywell; E. Gelenbe, Imperial)

This task will have a close affinity to technical area 1 (network theory) due to its focus on the networkservices within the sensor infrastructure. We would study the relationship between QoI and networkservices, such as routing and energy management.

Relationship between QoI and Routing. Network services such as routing and energy managementwill have significant impact on quality of information propagated through the network. For instance, thereliability of delivery information is closely coupled with the traveling distance, and the latency will beaffected by the offered load on the path and the traveling distance. The stability of an end-to-end path willimpact on not only the probability of out-of-order packet delivery, but also the susceptibility to trafficanalysis attack [10]. The goal of this task is to build a systematic analysis framework to evaluate therelationship between network services and the QoI. Good understanding of this relationship would give usinsights to further develop constructive algorithms for the co-design of data fusion architecture andnetwork services that maximize the QoI provided by the sensor network while minimizing its energyconsumption. The initial focus of this project will be network services for routing and energymanagement and how they affect QoI.

Our approach to analyze the relationship between routing structures and the QoI will include thefollowing components.

Survey of fusion schemes: Since the achievable QoI is closely related to the fused fusion structure andour ultimate goal is to develop a co-design mechanism for data fusion and routing architecture, abasic understanding on principles of sensor fusion structures and algorithms will be critical.Collaborating with Project 8, we will specify requirements and constraints from fusion architecture onnetwork services.

Survey of routing mechanisms: Most sensor network routing protocols have been proposed tomaximize a set of specific QoI attributes. For instance, SPEED [11] targets to reduce end-to-endlatency of information delivery whereas GRAdient Broadcast (GRAB) [12] proposes a fault-tolerantrouting structure to improve reliability and integrity. A one-fits-all routing solution to optimize allQoI attributes, however, will be extremely challenging as QoI attributes will have conflicts and trade-offs among them (such as a trade off between reliability and latency). In this review, we will clarifywhat characteristics or components (such as path selection mechanism and path discovery scheme) ofrouting protocols will have impact on QoI attributes and scrutinize decomposability of thosecomponents for the analysis study. Then, a primitive exploration on the integration feasibility of thosecomponents to design an optimal routing scheme with respect to multiple QoI attributes will follow.

Trade study: The trade-offs between QoI attributes or impacts of a routing component on each QoIhave not been well deliberated in the existing literature. Based on outcomes from literature survey, wewill develop an analysis framework using a simulation tool for the trade study. The framework willconsist of a generic and based routing model, a set of decomposable routing components, and QoImetrics. Various combinations of routing components added to the generic routing model will beevaluated and analyzed according to QoI metrics. Throughout this task, decomposable routingcomponents and QoI metrics will be further refined. This analysis framework will be a foundation of


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a mathematical analysis tool to quantify the relationship between network services and the QoI incoming year’s research.

Relationship between QoI and Energy Management. In a sensor network (SN), routing problemscan be avoided if each node is allowed to transmit at a power level that will allow it to reach thedestination or egress node directly. However, the power levels that may be needed so that any node canreach the egress node in one step may have several undesirable consequences:

Inter-node interference, requiring nodes to transmit at even higher levels, Inter-node collisions resulting in the need for further retransmissions, which would also reduce

the power life-time of nodes, and High transmission power in general reducing the life-time of the nodes.

Thus multi-hop communications using “other nodes” as relays needs to be considered. When multi-hop communications are considered, we again have interference and collision problems at a smaller scale,and simple broadcasting of packets would again be a poor solution for all of the reasons given above. Thisleads us to the question of routing. However packet routing does not come free of charge, because itrequire that nodes or relays be more intelligent and hence that they consume more power, and routinginformation may have to be conveyed in the network, which would again increase power consumption.Thus power management and routing in sensor networks are intimately related, and they also impact thequality of information (QoI) of the SN in a variety of ways. Even though the definition of QoI is part ofthe work of the project’s first year, we have to start our research with some intuitive understanding ofwhat QoI may mean for a SN. As an example, consider a SN which, in the simplest case, measures somephysical environmental variable in space and time. Thus the SN’s information can be viewed to be perfectif its output matches the ground truth. Let the ground truth be the variable g(x, t) where x denotes the 3-Dspace variable and t is time. Consider the SN output to be for each t, {s(x, t+T): x ε C} where C is thecoverage volume of the SN and T>0 is the delay with which the sensor network’s output is provided. Thesimplest view of the QoI of the SN would be a function providing some observed difference d(x, t) = ||s(x,t+T)-g(x, t)|| for points x ε C. In many cases, the set C may itself be part of the QoI measure of the SN,and quite naturally T would also be included in the evaluation of the QoI. Clearly, the set C will becomesmaller as certain sensors or relays lose power leading to larger errors expressed by d(x, t). Similarly, poorrouting increases T, leading to less timely information. Thus the work we will undertake in the first yearof the project will examine methods that can improve the power life-time of the SN and reduce the delayswith which information reaches the egress node(s). We will consider the energy budget E(n) per node n,and an overall energy budget E for the SN. We will consider the “smartness” of routing as a directionalparameter b, where b<0 if directionality is good, and b=0 if it is “stupid”. There will be an energy cost ofrouting expressed as a function e(n, b)>0 which grows as b becomes smaller. We expect to be able tocompute T(b), the delay with which data is conveyed to the egress node, as a function of b and as afunction of the density d of nodes, which in turn will be a function of the power consumption e(n, b) andof the energy budget E(n) per node. Through this formal approach, we expect to derive trade-offs betweenrouting intelligence, delay in gathering information, and the life-time of the SN in terms of its power.

Task 3: Sensor Characteristics and CalibrationResearchers: M. Srivastava and D. Estrin, UCLA, Ping Ji, CUNY.

The QoI delivered by a sensor network is adversely impacted not only by the non-idealitiesencountered in the network services such as routing as discussed in Task 2, but also by data integrityproblems and uncertainties at the sensor transducer and in the sensing channel, and sensor deploymentand usage. This task will study the relationship between sensor characteristics and the delivered QoI.Understanding this relationship is important for: developing more effective data fusion architectures;developing procedures for diagnosis and remediation of sensor integrity problem; and, for efficientresource usage by identifying and discarding bad data at the source itself.


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There are many potential causes because of which data is corrupted or lost at the sensor itself.Examples include noise in the environment; problems in the sensor such as aging, stuck-at values, anddrifting sensor parameters such as zero-offsets and gains; problems in transducer electronics such asthermal noise and saturation; biofouling and obstacles the sensing channel between the source and thetransducer; and, malfunctions such as failures, bugs and resource overflows in the sensor processinghardware and software. While smart sensors have been devised with outstanding calibration stability viafeatures such as stable gain, auto zero-offset correction, transducer interference compensation, andcompensation for temperature, package strain etc., the higher cost and power requirements of suchsensors often restrict their use to the higher-tier nodes in a deployment, as opposed to the lower tiers of asensor network where large number of cheap devices are the norm. The assumption that factory calibratedsensors purchased from vendors will produce accurate readings is false in practice, and significant per-sensor pre-deployment calibration and run-time re-calibration is needed in practice. Indeed, sensor dataintegrity has been a major issue across many sensor network deployments across various applicationsdomains reported in recent literature [7,8,9], and sensor data that is missing, outside the operating range,or punctuated by anomalous patterns is quite common. Integrity problems have included chemical sensorswhose internal solutions leaked with aging, sensor misbehaviors due to battery behavior, missing andcorrupted data due to sensors that drifted, snow and leaves that fell on the sensors, vehicular and otherobstacles that resulted in reduced sensor coverage, wind speed that affected acoustic sensors, changes innode location and orientation due to factors such as wind and animals, change in air clarity, and failures ofnode hardware and software.

Our approach to understanding the relationship between sensor characteristics and QoI will haveseveral components. First, we will develop realistic models of anomalous behaviors that sensors exhibit.While there exist in literature models such as Gaussian models and Interval models that seek to modelnon-ideal sensors, they unfortunately are too simplistic to capture the real-life behaviors that are observed.Moreover, sensors are based on quite diverse physical mechanisms and operate under different ambientconditions, and therefore a one-size-fits-all model is not effective. We will therefore conduct a detailedstudy of characteristics of a range of sensors to understand the failure modes and expected anomalies.This will be accomplished using detailed analysis of data sheet specifications, publicly available sensordatasets, and experiments that will conduct using different controlled environments with referencesensors. Our focus will be on sensors that are relevant to defense applications and where the sensor dataintegrity problems are particularly significant. Specifically, we intend to study sensors relevant to trackingapplications (acoustic beamforming and image capture with magnetic, PIR, and acoustic-level tripwires),and for chemical sensing applications. Second, based on the study of sensor behaviors we will developmodels of sensor integrity (transducer behavior and sensing channel conditions) that are suited foranalysis and simulation of higher layer mechanisms. Both physics-based and statistical parametric modelsand non-parametric statistical models will be explored and characterized in terms of fidelity anduncertainty. Eventual goal will be to integrate the developed sensor integrity model in a sensor networksimulation and development framework. These models will also help guide future work under this projecton developing techniques for detecting, diagnosing, and remediationg sensor integrity problems. Third,using the models developed we will conduct a simulation study of how common sensor data fusionmechanisms react to and cope with various sensor integrity failure modes as opposed to just plain noisethat these mechanisms are often designed for. This will not only help quantify the relationship betweenindividual sensor characteristics and the QoI delivered by the network, but also help guide future yearresearch into more robust fusion techniques and QoI-aware network mechanisms and resourcemanagement. Finally, we will engage the sensor network user community to understand how sensors aredeployed and used today in order to characterize their effects on QoI.


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Sensors and sensor networks are key components of the US and UK visions for their future forcetransformation programs. These programs are critically dependent on very quickly closing the sensor toshooter loop and rely heavily on quality information from their sensor networks. Today there is no way ofestimating the quality of the information provided by a sensor network. It is therefore impossible for adecision maker to act on the information provided by a sensor network without additional corroboratinginformation obtained by other means. Such corroborating information may be difficult or impossible toobtain in a timely manner thereby making the goal of timely and sound decision making unachievable.

This QoI framework developed by this research will fill the major technology gap described aboveand will help the US and UK militaries realize their visions for the future.


The entire project team will collaborate on developing a model for describing and expressing QoI.QoI is a multi-dimensional by nature and is a result of complex interactions between multiple aspects ofthe sensor network. It is therefore essential that experts in each of these various aspects work together todevelop a QoI model that is expressive and useful.

This project team will work very closely with Projects 8 and 9 as they are currently the primary usersof the QoI models developed by this project.

In Year 1 the project team will closely follow the work by TA1 in the area of fundamental limits ofnetworks as this research will have a bearing on the QoI delivered by a sensor network. Much closercollaboration with TA1 is envisioned for the following years.

The project team has also identified the need to work closely with TA2 in the area of security policies.The effect of security policies such as data encryption needs or data routing restrictions on QoI needs tobe studied. Conversely, the setters of security policy may need to be cognizant of the QoI implications oftheir policies.

In Year 1 we will pave the way for staff rotations and student exchanges by inviting project membersto make seminar presentations at each others universities and laboratories. Specific plans for theserotations and exchanges will be made during the course of Year 1.


QoI has been investigated, implicitly or explicitly, in a number of specialized domains. For example,the US Military’s Single Integrated Air Picture (SIAP) defines a number of quality attributes to measurethe adequacy and fidelity of information used to form a common understanding of the tactical situation.The proposed research will develop a QoI framework that can be applied to a wide variety of domains,especially sensor networks.


Research Milestones


Q1 Task 1 Survey of existing work of Quality of Information architectures. Involve indefining what QoI is in sensor network. Emphasis on: metadata frameworks(IBM-US), data cleansing and tomography (CUNY).

Q1 Task 2 Survey current sensor routing protocols and sensor fusion architectures andalgorithms. Characterize a baseline and constraints on network servicesrelated to fusion architecture (Honeywell)

Q1 Task 3 Study and development of a taxonomy of sensor characteristics and integrityfailure modes for a set of selected sensors relevant to defense applications


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Research Milestones


based on literature and public data sets. The results will be described in atechnical report (UCLA).

Q2 Task 1 Create a taxonomy of past research activities on metadata frameworks (IBM-US); have preliminary draft of problem definitions for data cleansing and QoItomography (CUNY).

Q2 Task 2 Define components of routing and energy management schemes that haveimpacts on the QoI and qualitatively evaluate effects. Prepare a preliminaryreport on the outcomes. Develop a skeleton of analysis framework for thetrade study. (Honeywell)

Q2 Task 2 Construction of a model of that allows the estimation of travel delay througha multi-hop sensor network (Imperial).

Q2 Task 3 Conduct controlled experiments with select sensors in diverse environmentswith reference sensors for ground truth to collect statistics on types andfrequency of integrity problems. The results will be described in a technicalreport.

Q3 Task 1 Develop an initial metadata framework (IBM-US); refine carefully onresearch problems definition and formalization and conduct correspondingstudy on the formalized problems (CUNY).

Q3 Task 2 Develop an initial analysis framework consisting of a generic routing model,a set of routing components, and the QoI measures using a simulation tool.Along with the framework development, refine decoupling of routingcomponents and research problems. (Honeywell)

Q3 Task 3 Development of analytic/numerical models of sensor characteristics andintegrity based on data sheets, public data sets, and observations of controlledexperiments. The results will be in the form of source code and associatedtechnical report.

Q4 Task 1 Share metadata framework to project collaborators and tabulate feedbackreceived on the framework (IBM-US); perform simulation and analysis(CUNY).

Q4 Task 2 Continue development of the analysis framework. Evaluate and analyze therelationship between routing layers and the QoI through the analysisframework. Present key observations and results in a technical report.(Honeywell)

Q4 Task 2 Computation of T(b), the delay with which data is conveyed to the egressnode, as a function of b and as a function of the density d of nodes, which inturn is a function of the power consumption e(n, b) and of the energy budgetE(n) per node.

Q4 Task 3 Simulation-based analyses of how common sensor fusion and estimationalgorithms react to sensor characteristics and integrity failures. The resultswill be in the form of source code and associated technical report.


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10.7. References

[1] H. Carvalho, W. Heinzelman, A. Murphy, and C. Coelho, “A General Data Fusion Architecture,” Proc.of the 6th Int’l Conf. on Information Fusion (Fusion 2003), Cairns, Queensland, Australia, July 8-11,2003.[2] D.J. Russomanno, C. Kothari, and O. Thomas, “Building a Sensor Ontology: A Practical ApproachLeveraging ISO and OGC Models,” Proc. the 2005 Int’l Conf. on Artificial Intelligence (ICAI’05), LasVegas, Nevada, USA, June 27-30, 2005.[3] S. Jeffery, G. Alonso, M. Franklin, W. Hong, and J. Widom, “Declarative Support for Sensor DataCleaning,” Proc. of Int’l Conf. on Pervasive Computing, 2006.[4] Mark Paskin and Carlos Guestrin, “A Robust Architecture for Distributed Inference in SensorNetworks”, Proceedings of IEEE IPSN, 2005.[5] R. Castro, M. Coates, G. Liang, R. Nowak, and B. Yu, “Network Tomography: Recent Developments,”J. on Statistical Science, pp. 499-517, 2004.[6] P. Buonadonna, D. Gay, J. Hellerstein, W. Hong, S. Madden, "TASK: Sensor Network in a Box," IntelResearch Berkeley Technical Report IRB-TR-04-021, January 2005.[7] Yu Hen Hu and Barbara Benson, “Sensor Network Data Quality Assurance,” Technical Report, ECEDepartment, University of Wisconsin, Madison, 2005.[8] K. Marzullo, “Tolerating Failures of Continuous-Valued Sensors,” ACM Transactions on ComputerSystems (TOCS), Vol. 8, No. 4, November 1990.[9] N. Ramanathan, L. Balzano, M. Burt, D. Estrin, T. Harmon, C. Harvey, J. Jay, E. Kohler, S.Rothenberg, and M.Srivastava, “Monitoring a Toxin in a Rural Rice Field with a Wireless SensorNetwork,” UCLA Center for Embedded Networked Sensing Technical Report #62, April 2006.[10] J.-F. Raymond, “Traffic Analysis: Protocols, Attacks, Design Issues, and Open Problems”, In LectureNotes in Computer Science 2000, pages 10-29.[11] He, J. Stankovic, C. Lu, and T. Abdelzaher, "SPEED: A Stateless Protocol for Real-TimeCommunication in Sensor Networks," International Conference on Distributed Computing Systems(ICDCS 2003), Providence, RI, May 2003[12] F. Ye, G. Zhong, S. Lu, L. Zhang, "A robust Data Delivery Protocol for Large Scale SensorNetworks," ACM Wireless Networks, 2003.


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11. Project 8: Task Oriented Deployment of Sensor DataInfrastructure

Project Champion: Thomas F. La Porta, Penn State

Email: [email protected] Phone: 814-865-6295


Derek Sleeman, University of Aberdeen Vic Thomas, Honeywell

Alun Preece, University of Aberdeen Boleslaw Szymanski, RPI

Amotz Bar-Noy, CUNY Chatschik Bisdikian, IBM-US

Ted Brown, CUNY Raju Damaria, ARL

Dinesh Verma, IBM-US TBA/RC Jitu Patel, DSTL

Andy Stanford-Clark, IBM-UK


The goal of Project 8 is to retrieve and disseminate information relevant to specific missions within arequired time-frame to maximize the utility of the sensor network. Maximizing utility encompassesgiving priority to more important missions, balancing the quality of information with the energy costs ofgathering the data, and storing and disseminating information in a manner so that it can be used mosteffectively.

Project 8 is broken into five tasks:8.1 Mission Scripts for Data Source Orchestration aims to develop initial representations of missions

that can be used to determine the data required for a mission.8.2 Modeling Repurposable Sensor Networks aims to model, through a sensor ontology, the sensors

and sources required by a mission.8.3 Reactive Source Deployment aims to re-purpose, move, or re-deploy resources to gather the

information that will maximize the utility of the network.8.4 Push Data Delivery aims to disseminate the gathered information in a timely manner to the

interested parties, for example sensors that require mission plans and status.8.5 Collaboration and Validation Environment task aims to provide a distributed test facility that can

be used for validation of algorithms through a combination of simulation and emulation.


P8 has strong relationships with projects in TA1 and TA4. TA1 is expected to provide fundamentalbounds on performance of access protocols under various network and communications constraints; thesewill be used to shape some of the algorithms within P8. These results will be used to drive thecharacterization and invention of communication protocols and algorithms for the sub-projects 8.3 – 8.4.8.1-8.3 will influence and be influenced by P2 of TA1 which deals with interoperability between networksat various layers. P8 will use and influence the ontologies and mission plans developed in TA4. In thismanner P8 and TA4 are integrated in several places.

We stress that in the first year it is difficult to positively identify specific results from TA1 and TA4that will be used because these projects have just begun and it is not possible to predict the definitiveresults. We will work with TA1 and TA4 to jointly develop results and monitor progress and ensure thatwe can maximize the impact of their results.



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Interaction with other Projects in TA3. The figure below summarizes the relationships between P8and P7 and P9 from the point of view of P8. P7 provides a measure and representation of the quality ofinformation (QoI). This metric is not mission specific, but rather sensor and phenomena specific. P9provides an estimate and representation of the value of the information in the context of a specificmission. This metric considers mission priorities. Algorithms from P9 will drive the algorithms in P8 toconfigure the network to maximize the utility of the network.

The algorithms of P8, unlike those of P7 andP9, make use of local observations of the sensorsand knowledge of their local state to configure thenetwork to balance the needs of multiple missionsto achieve the best possible network utility. Thesenodes will have portions of the mission plans andcharacterizations of the abilities of the sensors, inaddition to the local state and observations. Theconfiguration derived by these algorithms maynot always match the expected values from P7and P9 for a variety of reasons. An interestingresearch result will be the optimal distribution ofinformation amongst the algorithms: is it better tocentralize more configuration control in themanagement systems of P9, or to place moreknowledge and decision-making abilities in

sensor nodes. Once the network is reconfigured, the new configuration is fed to P7 and P9 so that theymay update the QoI and VoI metrics.

Interaction of sub-projects within P8. 8.1 and 8.2 provide the basis for the target networkconfiguration. By characterizing missions and sources required to meet mission requirements, a strategyfor satisfying the missions is formed. This is fed to the algorithms of 8.3 which reconfigure the network.These algorithms perform a dynamic analysis considering local state and real-time observations. Thealgorithms of 8.3 are also fed local observations of the sensor network via the algorithms of 8.4. The useof local observations may result in an outcome slightly different than determined by the input from 8.1,but will allow the network to be more reactive. 8.3 feeds back to 8.1 to update the state of resources, andfeeds forward into 8.4 for data to be disseminated. The algorithms of 8.4 are responsible fordisseminating information on many scales. 8.5 provides a shared facility to consistently characterize thesolutions.

Task 8.1: Mission Scripts for Data Source OrchestrationResearchers: (Sleeman and Preece/Aberdeen, Bar- Noy /CUNY)

The nature of a mission, including the nature of the terrain and the assailants (or those to be assisted)are the major determining factors of the data sources required. As stated in OTA-1, we plan to associateeach type of mission with several outline scripts (or plans). It is possible to see how such plans may becreated for rescue missions in which there will generally be a number of identifiable stages, each withinformation requirements such as maps of the relevant area, status of the rescue sites, and of the victims.Further, such plans would allow for specification of timeliness and fidelity requirements as well asdefinitions of what data sets are complementary, dependent, and substitutable. These plans and profileswill be used to predict the data gathering and data dissemination requirements of a mission, and will beused to reposition or deploy data gathering devices and for scheduling transmission of requiredinformation.

In year 1 we will attempt to define an initial representation for these scripts, and create a number ofmission instances that will feed into the larger TA3 and TA4 scenarios. Work on identifying requirements

Quality of Information (7)

Characterize and measure QoI

Deployment (8)

Configure network for missionsMeasure usefulness

Management (9)

Characterize Value ofInformation and drive network

configuration

QoIUpdated

Configuration

UpdatedConfiguration

VoI targets


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for the mission scripts began at the Cambridge workshop with the TA3 GTALs. The development of thescript representation is expected to be informed by:

available (declassified) mission scenarios, including those used in military training (e.g. ISTAR) review of relevant literature on scripts in AI, especially the Schankian notion of script [Schank &

Abelson, 1977], and the way scripts and goal-directed planning are used in MUSKRAT [White &Sleeman, 2000]

These scripts will be developed in conjunction with the work of P10 and P12: the mission plans willbe aligned with the command and control structures developed in P10, and will use the semanticbattlespace ontologies from P12. Aberdeen are engaged in both TA3 and TA4 and Preece will facilitatethis linked work. In fact, because P8 will focus on the information needs of a mission in terms of sensorsand sources, we expect to make progress ahead of P10 and P12 which address the wider range ofbattlespace assets, and can feed the P8 progress into the TA4 work.

Task 8.2: Modelling Repurposable Sensor NetworksResearchers: Sleeman & Preece/Aberdeen, Stanford-Clark/IBM-UK)

The basis of the tight coupling between the strategic, operations-management, and delivery elementsof P8 is a sufficiently rich specification of the classification of sensors and data sources. This model –which is likely to take the form of a sensor ontology – will need to characterize:

the various types of sensor and data sources available attributes, parameters, value ranges, and instance data for each type of sensor/source, such as

location, power availability, mobility potential, etc. the kinds of information (content) available from the sensors/sources the available control options for the sensors/sources the dimensions in which the sensor/source is “repurposable” (e.g. can it be relocated? can it be

configured dynamically to deliver a variation in the kind of data available? can it be remotelypowered-down and restarted later?)

the available data delivery options (broadcast, narrowcast, etc) options for storage/caching data on the sensor itselfIn defining this ontology, we will draw on some of the recent work in the description of Semantic

Web Services [Paolucci et al, 2002] and the work that will be undertaken concurrently in P12. We willalso examine related initiatives such as SODA (Service-Oriented Data Architecture).

It is not envisaged that the ontological descriptions of the sensors will be stored in the sensorsthemselves, as they are likely to be voluminous, but should be stored “in the network” at a logical andappropriate location, to enable the key information to be made available to all interested parties in adynamic way when new sensors enter and leave the messaging fabric.

To provide a concrete competency test for this ontology in year 1, we will aim by the end of Q4 tocreate a prototype that extracts the information requirements from a mission script and matches theseagainst instances of available sensor/sources. This will provide a tangible link between 8.1 and 8.2, andwill align with the testbed addressed in 8.5.

Task 8.3: Reactive Source DeploymentResearchers: La Porta/Penn State, Verma/IBM-US, Stanford-Clark/IBM-UK, Amotz Bar-Noy,

Theodore Brown/CUNY),

In this task we examine how to repurpose and reposition sensors in order to optimally meet theinformation processing and fusion needs of a sensor network for a set of missions. The mission scriptsdeveloped in tasks 8.1 provide an area which needs to be modeled by a sensor network. When severalportions of an scanned area needs to be monitored, the deployments of the different types of sensors needsto be used in a manner that is most efficient in order to monitor all of the areas properly.


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In the case of multimodal sensors, there are different energy requirements of different modes ofmonitoring. Some modes, e.g. acoustic monitoring, can be done with relatively minor energyrequirements which other modes, e.g. video monitoring, needs a large amount of battery power. Given atotal amount of energy constraints, one needs to determine the best mode of monitoring the target areasidentified by a mission script within the constraints of power and mobility constraints.

We start with a simple model of a static sensor deployment infrastructure and study the differentalgorithms under which the reactive source deployment schemes will be able to turn on different modes ofoperations of the sensors. We would initially begin with an analysis of sensors that are simply on/off,gradually extending the model to account for sensors with different modes and sensors which are mobile.We would like to analyze the processing of information from different types of mobile sensors, includingsensors whose motion can be controlled by our algorithms (e.g. sensors mounted on a blimp), sensorswhose motions are erratic, uncontrollable and unpredictable (e.g. sensors on the body of coalitionsoldiers) as well as sensors which are pilfered and captured by enemy combatants and miscreants. Wewould eventually model the mobility of sensors as well as constraints on sensor mobility. However, forthe first year, the focus will primarily be on sensor fusion and turning on different sensor modalities.

Our simple model describes a sensor field in a Euclidean space. We characterize the number ofsensors deployed in the field by its probability distribution function. Let p be a point in the Euclideanspace S; then the distribution of sensors in the field is given by the probability function f: S [0,1] such that f(p) is the probability of finding a sensor in a small area around the point p. A targetthat is intended to be tracked in the sensor field is characterized by a similar probability function g: S xT [0,1] such that g(p,t) is the probability of finding the tracked object at time t in a small areaaround the point p.

At any time, a sensor that is in the Euclidean space can operate in a variety of modes. We initiallyassume that each sensor can operate in one of two modes, on or off. The distribution of an active sensor ischaracterized by the probability function h: S [0,1] such that h(p) is the probability of finding asensor that is turned on in a small area around the point p.

An algorithm for adjusting the sensors field can be viewed as a transformation function on h(p,t).The transformation algorithm T can be characterized as a function from the space of all functions of typeh, so that T(h1) = h2, where h2 is another distribution function of sensors that are on. Assuming adiscrete count of time, we can envision the algorithm for sensor field to change the active sensor functionsto starting form an initial distribution of h0(p) to be h(t,p)) at time t, where h(t) = T(h(t-1,p)). The algorithm should satisfy the constraint at any given instance of time, sensors within a givenrange of the target should be turned on. In other words: g(p,t’) = 1 => h(p,t’) = 1.

In a well-designed algorithm, the set of sensors that are turned on h(t,p) should converge to thetarget of function g(p,t). Our goal is to address following:

Understand the conditions under which h(t,p) converges to g(t,p).

Understand for a class of algorithms, the time to converge to the final system. Obtain approximations for a system which has finite and discrete sensor positions.

We will propose and explore different transformation schemes, and assess how well they perform. Wewill extend the problem formulation to consider realistic mission models and multiple missions (8.1);realistic sensor models (8.2); to characterize the sensitivity to lost, delayed or incorrect local observations(8.4); and to develop new algorithms, initially based on greedy approaches [Wang et al, 2005, 2003]. Wewill also extend the work to consider the interaction between the data relaying and messaging capabilitiesof the network and the ability to reactively reconfigure the network.

Task 8.4: Push Data DeliveryResearchers: Amotz Bar-Noy, Theodore Brown/CUNY), Dinesh Verma, IBM-US.

The main objective of this task is to disseminate gathered information to clients. Clients include dataexploiters that require data in real-time and consumers that use data for longer term purposes. In the first


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year we focus on delivering mission requirements and data from the sensor field to sensors so that thealgorithms of 8.3 may be made reactive.

The data exists in an information center that has shared communication lines with its clients. Themain task is the design of an efficient schedule to push the gathered information to all clients so as tooptimize pre-defined goals that are important to some specific applications. The problems addressed bythis task fall into the category of periodic scheduling problems. While this is a well-studied problem, aslight change in the variant and/or in the objective function may require a vastly different solution. Thecontext of scheduling mission requirements, and data to enable the completion of missions, each withdifferent priorities, is a challenging one.

Periodic scheduling is the task of scheduling competing jobs on several machines while obeying themutual exclusion property: no two jobs are scheduled on the same machine at the same time. Theschedule should find a way to share the machines among the jobs so as to optimize some objectivefunctions. The objective functions depend on the gaps between consecutive appearances of the same dataitem that reflect the maximum delay suffered by clients who access this data item.

More formally, in the basic setting, there are n data items denoted by d1,…,dn to be broadcast on onebroadcasting channel that is accessed by many users. Time is slotted; we assume it takes one unit of timeto broadcast a data item. A schedule is an assignment of data items to time slots. A common extensionassumes each data item is associated with length in number of time slots needed to broadcast the dataitem. In the non-preemptive case, all the slots allocated to any data item must be consecutive. In thepreemptive model, the broadcasting of the fragments of data item dj need not be in consecutive time-slots. In another common extension, there are more than one broadcasting channels.

Most of the periodic scheduling problems are NP-hard. Thus, we will be looking for near-optimalsolutions. We will try to find solutions with performance guaranteed (compared to the performance of theoptimal solutions) and solutions that perform well in simulation environments that represent the variousapplications. The following are some important known techniques that we plan to adopt. (i) In greedysolutions the scheduling decisions are made at each time slot depending on the urgencies that are definedby the objective functions. These solutions are good for dynamic scenarios. (ii) We will apply regularsequences (e.g., the golden ratio sequence) that are based on the given shares that are associated with thedata items and are proven to behave well for some objective functions (iii) We will consider periodicschedules for applications that require clients to efficiently manage their time and power.

As this work matures we will consider realistic mission priorities and data requirements (8.1) andtailor our algorithms to consider realistic error rates and channel capacity (TA1).

Task 8.5: Collaboration and Validation EnvironmentResearchers: Stanford-Clark/IBM-UK, all of the team.

During year 1, there will be ideas and hypotheses for the form and function of 8.1-8.4. It will bevaluable to be able to test these hypotheses and experimental systems on a tangible sensor andcommunications network. 8.5 will create a plausible messaging software layer on a limited network ofsensor and other network nodes, making as much use as possible of existing COTS software (from IBMand others as appropriate). Whilst it is clearly recognized that this will not provide any type of definitivesolution, it will nonetheless provide a functional test bed on which experimental algorithms can beobserved and analyzed.

IBM has a small, embedded messaging broker, called the MicroBroker, which provides publish/subscribemessaging on small devices (code footprint <1MB, written in Java, can run on a 16M RAM machine).The pub/sub approach results in a reconfigurable "data stream" model using topics between data sources,data sinks, and a network of inter-connected MicroBrokers. We will use this message broker toimplement a testbed that spans all five research sites for P8, and use some simple sensors (e.g. ambienttemperature) as sample data sources.


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This project enables the military vision of delivering required information to the correct parties ontime with optimal use of resources. This provides warfighters with access to the right amount ofinformation at the time it is needed increasing the tempo of ongoing missions. By prioritizing missionsand data importance, sensor and network resources are used optimally according to power constraints, andpotentially risk factors such as the possibility of being destroyed. This extends the life of the sensornetwork further increasing its utility.


The collaborations between the tasks are outline above according to team members. It is expectedthat several short-term rotations (1-2 weeks) will occur between Aberdeen and IBM-UK, and betweenPSU and CUNY and IBM-US in the first year. Further rotation of students and PIs for potentially longertime periods (order of a semester) will be planned for the second year.


There is no known relation or overlap with any of the ongoing DoD/MoD research programs.


Research Milestones


(A = Aberdeen, C = CUNY, P = PSU, U = IBM-UK, S = IBM-US)

Q1 Task 1 Identification of sources for developing mission scripts (Draft TR(DTR)): A

Q1 Task 2 Identification of knowledge elicitation sources for sensorontology (DTR): A, U

Q1 Task 3 Formal problem definition and initial mission functions (DTR): P,S, U

Q1 Task 4 Formulate a basic setting and propose initial solutions based onknown methods and techniques (DTR): C

Q1 Task 5 Initial definition of a rudimentary test bed using COTScomponents: U

Q2 Task 1 Initial requirements for mission scripts, focusing on informationneeds of a mission (DTR): A, C

Q2 Task 2 Initial scoping for the sensor ontology: breadth, depth, andcompetency questions (DTR): A, U

Q2 Task 3 Initial algorithms considering on-off sensor model for sensorrepurposing for mission scripts (DTR): P, S, U

Q2 Task 4 Extend the basic setting to a more general setting that will capturemore general scenarios (DTR): C

Q2 Task 5 Construction of a rudimentary messaging fabric across a networkof sensors;

Evaluation of utility to P8 (DTR, software): U


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Research Milestones


(A = Aberdeen, C = CUNY, P = PSU, U = IBM-UK, S = IBM-US)

Q2 All Identify collaborators in TA1, TA4: all

Q3 Task 1 Initial representation for mission scripts (TR): A

Q3 Task 2 Initial version of (at least part of) the sensor ontology (DTR): A,

Q3 Task 3 Initial algorithms for observing initial areas identified by missionscripts using multi-modal sensors (TR of Q2, DTR of Q3): P, S, U

Q3 Task 4 Propose more involved and better solutions; start constructing asimulation environment to test and compare solutions (TR for Q2,DTR): C

Q3 Task 5 Iterations of the experimental test bed to include additionalfunctions based on feedback and experience (Testbed): U, all

Q3 All Establish staff rotation schedule for year 2: all

Q4 Task 1 Prototype reasoner that can identify the information needs of amission given a script (Software, TR of Q3): A

Q4 Task 2 Prototype reasoner that can select instances of sensor/source tomatch information needs (Software, DTR): A, U

Q4 Task 3 Start on algorithms for multiple missions; consider sensitivity tolost or delayed local information (TR for Q3; papers (1-2) submitted):P, S, U

Q4 Task 4 Integrate work with 8.1 and 8.3; start consideration of dynamicsystems and pull delivery systems (TR Q3, paper(s) submitted): C, P

Q4 Task 5 Experimental messaging test bed available for evaluation ofalgorithmic hypotheses from 8-1-8.4 (TR): U, all

The majority of rotations in the first year will be 1-2 weeks. Of the budget, approximately $14,000will be spent on these types of visits ($2K for Aberdeen and CUNY each, $5K for IBM-UK, and $5Kfrom Penn State).

11.7. References

M. Paolucci, T. Kawamura, T.R. Payne, and K. Sycara “Semantic Matching of Web ServiceCapabilities,” in Proc. ISWC-2002, 2002.

R.C. Schank and R.P. Abelson, Scripts, Plans, Goals, and Understanding, Laurence ErlbaumAssociates, 1977.

S. White and D. Sleeman “A Constraint-Based Approach to the Description and Detection of Fitness-for-Purpose,” Electronic Trans. on Artificial Intelligence, 2000.

G. Wang, G. Cao, T. F. La Porta, “Movement Assisted Sensor Deployment,” IEEE Transactions onMobile Computing, Vol. 5, No. 6, June 2006, pp. 640-652.

G. Wang, G. Cao, T.F. La Porta, “A Bidding Protocol for Deploying Mobile Sensors,” Proc. of IEEE.


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12. Project 9: Complexity Management of Sensor Data Infrastructure

Project Champion: Boleslaw K. Szymanski, RPI

Email: [email protected] Phone: (001) 518-276-2714


Boleslaw Szymanski, RPI, US Tom La Porta, PennState, US;

Shuqun Zhang, CUNY, US Vic Thomas, Honeywell, US

Mark Nixon, U. Southampton, UK Nigel Shadbolt, U. Southampton, UK

Graham Bent, IBM, UK Mani Srivastava, UCLA, US

Andrew Reynolds, IBM, UK TBA/RC Jitu Patel, DSTL

Mandis Beigi, IBM, US

Gavin Lock, Logica, UK


This research project will develop techniques to reduce the complexity of managing sensor datainfrastructure. It will expose a simplified control interface for managing the multitude of disparate sensingand processing element. Furthermore, it will develop data fusion algorithms which include semanticinformation to reduce the burden of understanding data sources (sensors) and how they can be effectivelybe deployed. This research will reduce management information overload and will significantly improvesensor network survivability, and will enable improved process synchronization among coalition partners.

The project will research and develop techniques for reducing the complexity of managing datainfrastructure in a sensor environment and in the first year, it will consist of four research tasks: (1)multimodal sensor fusion algorithms for object detection and tracking, (2) parameterization of sensornetwork algorithms, (3) mission driven infrastructure management, and (4) knowledge-based informationfusion algorithms via Semantic Web technologies. These tasks will be conducted in close collaborationwith projects 7 and 8 in Topic Area 3, as well as with projects 1, 3, 6, 10, and 12. Hence, during the firstyear we will also develop those requirements for the research in the collaborative projects that are themost relevant to our needs in project 9.

Task 1 focuses on investigating the use of multimodal sensor fusion to reduce data managementcomplexity, and improve system performance under the context of target detection, tracking, andrecognition. Most existing data fusion works on unattended ground sensor networks for target detectionand tracking so they are restricted to fusion of sensors of the same modality, use of dense sensor networkfor sensing accuracy, and fusion of data on higher levels only. For example, dense acoustic sensors arenormally used due to the low cost and power usage. We propose to exploit multimodal sensors to improvesystem’s detection and tracking performance while reducing computational requirements, and at the sametime to reduce the sensor density and thus data management complexity. The major research issues to beaddressed include how to determine the best modalities given a set of different types of sensors andenergy and performance constraints, and how to best fuse the data from the identified modalities indifferent levels.

Task 2 focuses on parameterization of sensor network algorithms, primarily for routing, fusion anddistributed data processing. Such parameterization will guide dynamic and optimal (in certain metric)sensor network adjustments to changing mission requirements. Parameterization of sensor network


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algorithms will focus on their diverse roles, such as: (a) sensing: how the quality and cost of sensingalgorithm depends on such technical parameters as frequency of sensing session, coverage of the sensingarea, redundancy level of the coverage both in space and time, spectrum and granularity of the sensedphenomena measurements; (b) computing: how the results and their quality as well the costs (especiallyin terms of energy and memory) of algorithm execution depend on frequency of computing of thecollected data, scope of the data included in the computation (both in time and space), locality ofprocessed data, and volume reduction of the results versus raw data; and (c) communicating: how theenergy cost, network reliability and the quality of the results depend on volume (especially in view ofbandwidth constraints) and frequency (especially in view of energy constraints) of communication, powerand redundancy of the network communication (tradeoffs between the transmission power level and rangeof transmission, density of the network deployment versus sleeping/awake protocols for individualnodes), data aggregation versus data accuracy, and local versus centralized data processing.

Management of a large-scale distributed sensor infrastructure, the focus of Task 3, remains largely un-addressed system problem. A hierarchical control structure is the key to the solution of this problem,where the control of heterogeneous local sensor networks can be delegated to edge devices that performthe functions of aggregating and communicating local status to mission command and relaying controlfrom mission command back to a given sensor environment. We envision a multi-level hierarchy, inwhich edge devices interact with many wireless sensor nodes at the lower level of the hierarchy and at thesame time they communicate with many nodes on the higher level of the hierarchy. Data gathering, datacollection and pre-processing are mainly conducted at the lower level of the hierarchy whereas datafusion mainly operates at the higher levels, supporting separation of concerns. Scalability is the primaryconcern, so clustering of data to reduce the volume, speed and types of data sent to higher levels will beaddressed. We will focus on the processing in dynamically changing network setting and on the higherlevel processes and the communication mechanisms.

Task 4 concentrates on semantically-mediated fusion to reduce the burden of understanding datasources by exploiting the new descriptive power of semantic technologies, and thereby afford new insightinto the data and its processing. Of explicit interest is the parameterisation of trust and uncertainty andtheir contribution to the fused classification decision, and its use to achieve additional capability withinthe aims of sensor information processing and delivery. This will extend the current interest in buildingontologies for military use, and extend their use within a data fusion framework. It should also allowleverage of existing research in probabilistic fusion and aim to improve performance thereof. Analternative point of view is that this new framework will allow for investigation of, and capitalization on,the covariate structure in classification and in decision making, which is a theme current amongst manyapproaches in applied pattern recognition. As such we shall aim to reduce complexity whilst concurrentlyaiming to improve deployment capability, consistent with major aims of complexity management.


Task 1: Multimodal Sensor Fusion Algorithms for Object Detection and TrackingResearchers: (Shuqun Zhang, CUNY, Mani Srivastava, UCLA)

Our technical approach use cooperative processing, statistical models and analytical methods togetherwith resource constraints.

Considerable research has been conducted on multisensor fusion. However, the fusion of differentmodalities of sensors has not yet been adequately addressed, especially in unattended ground sensor


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networks, which traditionally employ single-modal sensor arrays in object location for the sake of lowerdeployed system cost and energy usage. They require high spatial density of sensors and inter-sensorcommunications for sensing accuracy and wide range coverage, which complicate the communication anddata management. It is therefore necessary to exploit multimodal sensor fusion to improve performanceand also reduce data management complexity.

The use of multimodal sensors has the potential to significantly improve a system’s performance intarget detection and tracking by introducing complementary information. This is especially true in areas inwhich there is a dense target environment, rapidly maneuvering targets or complex signal propagationenvironments. To exploit sensors of different modalities, we will need to identify the features of eachtype of sensor and the information that they can provide. Based on battery power and sensing accuracyconstraints, the best modalities will be identified for a particular mission. The energy consumption andperformance requirements also determine which target detection and tracking algorithm can be used for asensor. Information from other types of sensors can be used to assist in searching for the energy-efficientdetection algorithms since some information provided by one type of sensor might be able to simplify thecomputation in other types of sensors. Once the best modalities have been identified, data fusion isperformed to utilize the redundant, complementary and timely information of multiple sensors. Theusability and reliability of data from different sensors are measured and sent for fusion.

While fusion of different modalities usually happens at the feature level, we will investigate themultimodal fusion on multiple levels since different levels of fusion can provide information to a systemfor different operations. For example, it is possible for a sensor to directly influence the operation ofanother sensor to achieve better performance in lower level fusion. Statistical approach will be mainlyused in sensor modeling and fusion. The fusion results from different levels can be fed back to improvethe detection operation. Multimodal sensor fusion is also proposed as a method to reduce the number ofsensors in performing detection and tracking for the same area of coverage. This is possible by efficientlycombining long-range and short-range sensors. The optimal number of sensors can be estimated under theconstraints of energy, communication and detection accuracy. Our investigations for this task will focuson using cooperative processing, statistical and analytical models together with resource constraints andsimulations.

Task 2: Parameterization of Sensor Network AlgorithmsResearchers: Boleslaw Szymanski, RPI; Shuqun Zhang, CUNY

Quantification of the quality of the results of processing in sensor networks: (a) precision: what is theerror on the values of the processing results, (b) correctness: what is the probability that the results aretrustworthy, (c) latency: how much the data are delayed compared to the current situation in the field, and(d) long-term cost: how the algorithm execution with the selected parameters impacts the probability ofsensor detection (for example through active communication or sensing), time to live of the sensornetwork as defined by energy constraints or its ability to perform other needed algorithms. An importantaspect of the corresponding research will be sensitivity analysis of the quality measures researched inproject 8 to the selection of parameters researched here. In both cases, the algorithms that will beparameterized and quantified will include those contributed by the researchers participating in this task aswell as researchers in other tasks of this proposal. In evaluation of different solutions to theparameterization and quantification of the sensor network algorithm we will rely heavily both onextendable simulators (for example SENSE [P9-1], recently developed by us) as well as laboratory sensornetwork infrastructure available to us at RPI and other Consortium members, before their deployment inthe field.

Very similar parameterization and quantification will also be conducted on the data fusion algorithms,especially those specifically designed in response to the challenges identified in the above problems.Mission command decision makers should only be aware of high-level control parameters that give theminsight into the sensor network current or desired status in view of their current and future missions,


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whereas an autonomous control system should be able to drive the system to the optimal point for thechosen set of parameters. The key is the identification of a set of meaningful high-level controlparameters that can be translated to lower-level parameters of the optimization problem.

A crucial element of our technical solution will be the identification of a measure of usefulness/valueof an individual sensor’s information, as well the information derived from sensor data fusion. This willallow us to incorporate information fusion and compression into a formal optimization frameworktogether with system resource constraints, such as energy and bandwidth, and mission objectives. Thesecond key element of our solution will be to identify a set of higher-level mission control parameters,from which parameters of the optimization problem will be derived. The goal is to enable the decisionmakers to control process by defining an operating point of the system in a multidimensional space usinga subset of the most important parameters. The control system is then responsible for finding the exactoptimal point in the overall parameter space that corresponds to the specified parameters and to calculatea feasible, least costly trajectory for the system transition to this optimal operating point. We will alsoinvestigate market-based (e.g. auctions) for finding such a trajectory.

Our initial work in this area indicates that both communication protocols (such as routeless routing[P9-2] or community routing [P9-3]), as well as application algorithms such as outlier detections [P9-4]are amenable to such parameterization. This work will be done in close collaboration with projects 7(Mani Srivastava, Deborah Estrin) and project 8 (Tom La Porta).

Task 3: Mission Driven Infrastructure ManagementResearchers: Graham Bent, IBM, UK; Mandis Beigi, IBM, US; Andrew Reynolds, IBM, UK;

Boleslaw Szymanski, RPI, Gavin Lock, Logica

There are many similarities between the challenge of managing a distributed sensor infrastructure andthe approaches being used in the IT industry to provide flexible and adaptive business processes thatmake use of disparate and distributed resources that may exist both within and between organizations. Toprovide for easier system integration, a standards based approach is adopted using web and grid servicesas the basis for the integration. One of the main challenges to providing the required flexibility andinteroperability is the lack of semantic understanding in the web and grid services and in particular theautomation of service discovery. The semantic web community has a range of proposals for automaticservice discovery, composition and mediation, however, how to add semantics of web service descriptionsand how to leverage the semantics at appropriate times in the solution life cycle are still open questions.We will research design and management of the sensor infrastructure with specific focus on how semanticweb technology can be combined with current practice.

Management of a large-scale distributed sensor infrastructure remains largely unaddressed systemproblem. A hierarchical control structure is the key to this problem, where the control of heterogeneouslocal sensor networks can be delegated to edge devices that perform the functions of aggregating andcommunicating local status to mission command and relaying control from mission command back to agiven sensor environment. (An example of a status retrieval operation is checking the percentage of activesensor nodes forming a sensor network. An example of a control operation is setting the energy level orcommunication mode of a sensor network.) We envision just two-level hierarchy, in which edge devicesinteract with many wireless sensor nodes at the lower level of the hierarchy and at the same time theycommunicate with many nodes on the higher level of the hierarchy. Data gathering, data collection andpreprocessing is mainly conducted at the lower level of the hierarchy whereas data fusion mainly operatesat the higher level, supporting separation of concerns. Scalability will be addressed by clustering ofincoming data to reduce the volume, speed and types of data sent to the higher level. This process will beguided by intrinsic, retention and perishability values of data and will involve also data correlation.

We will focus on applicability of the current service oriented (model driven) architecture approach tothe management of the sensor infrastructure. The research will focus on the applicability of the currenttools and methods to the management of sensors in an ad-hoc wireless network. This will include all


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layers of the SOA architecture including mission tasking, process choreography, service composition,service discovery and optimized deployment. The research will investigate the following areas:representation of a distributed sensor management process in an SOA architecture; use of sensor modelinglanguages (e.g. SensorML [P9-5]) to represent the sensors and sensor processing as sensor services; theuse of sensor ontology’s such as OntoSensor [P9-6] that can assist in the semantic discovery of sensorservices; dynamical configuration of multi-modal sensors represented as services. The overridingobjective in the first year is to determine where existing model driven approached to the sensorinfrastructure management challenge cannot be met with the existing approaches and where and whatchanges need to be made.

Task 4: Knowledge-Based Information Fusion via Semantic Web TechnologiesResearchers: (Mark Nixon, Southampton and Graham Bent, IBM UK)

We shall include parameterizations of trust and uncertainty in a new semantically mediated datafusion framework to improve performance, deployability and operational capability within network-enabled capability. This implies that we shall need to parameterize trust and uncertainty in order to controltheir contribution to the new framework. This parameterization will happen in collaboration with project6 (trust and risk management) in technical area 2. Currently, we consider uncertainty to be a quantifiablemeasure for use within a probabilistic framework (e.g. [P9-7]) and we shall need to parameterize trust toaugment this within a semantically-mediated framework. We shall also need to characterize the nature ofthe data to be analyzed by the new approaches especially in the feature extraction stage to provide ameasure of uncertainty to the fusion process (in biometrics, a statistical framework exploring covariatesused ANOVA [P9-8]). A feature extraction process is normally used in classification; other scenarios arelikely to depend more on the semantic framework. Accordingly, we shall need to refine a probabilisticapproach to fusion and then refine and deploy an ontological approach (e.g. [P9-9]) to allow for analysisand improvement of the new approach. We anticipate that the initial construction of this framework couldbe complete within the first year of this project, as shown within the project’s deliverables. The project’smain links with project 12 concern its aim to provide a flexible means to enhance communication andcoordination in DCPDM via adaptive ontologies and the means to develop appropriate courses of actionvia planning and decision making tools and techniques.

12.3. References

[P9-1] G. Chen, J. Branch, M. Pflug, L. Zhu, and B. Szymanski, “SENSE: A Wireless SensorNetwork Simulator,” Advances in Pervasive Computing and Networking, Springer, New York, NY, 2004,pp. 249- 267, 2004.

[P9-2] G.G. Chen, J.W. Branch, and B.K. Szymanski, “Local Leader Election, Signal Strength AwareFlooding, and Routless Routing,” 5th IEEE Int’l Workshop on Algorithms for Wireless, Mobile, Ad HocNetworks and Sensor Networks, WMAN, 2005.

[P9-3] J. Branch, G. Chen and B. Szymanski, “ESCORT: Energy-efficient Sensor NetworkCommunal Routing Topology Using Signal Quality Metrics,” Int’l Conf. on Networking, 2005,

[P9-4] J. Branch, B.K Szymanski, C. Giannella, R. Wolf, and H. Kargupta, “In-Network OutlierDetection in Wireless Sensor Networks,” 26th Int’l Conference on Distributed Computing Systems, 2006.

[P9-5] Botts, M (2005), “OpenGIS® Sensor Model Language (SensorML) ImplementationSpecification” Open Geospatial Consortium, Inc.


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[P9-6] D.J. Russomanno, C. Kothari and O. Thomas (2005) “Building a Sensor Ontology: A PracticalApproach Leveraging ISO and OGC Models,” The 2005 International Conference on ArtificialIntelligence, Las Vegas , NV, pp. 637-643.

[P9-7] A.I. Bazin, and M.S. Nixon, “Probabilistic Fusion of Gait Features for Biometric Verification”Proc. 8th Int. Conf. on Information Fusion, 2005.

[P9-8] G. Veres, J. N Carter and M. S. Nixon, What image information is important in silhouette-based gait recognition, Proc. IEEE Computer Vision and Pattern Recognition 2004, 2004

[P9-9] Lawrence, K. F., …, Nixon, M. S., and Shadbolt, N. R., OntoMedia - Creating an Ontology forMarking Up the Contents of Heterogeneous Media, Proc. Ontology Patterns for the Semantic Web ISWC-05 Workshop, Ireland. 2005


There are several key aspects of managing data and infrastructures in a distributed, resource-constrained, multi-modal sensor environment, including the following. (i) Preventing informationoverload while capturing important information through intelligent distributed sensor data fusion. Thisinvolves defining and using a metric for quantifying the importance of information. (ii) Respectingresource constraints of the operating environment, especially energy and bandwidth in sensor networks.(iii) Managing and controlling the sensor infrastructure efficiently. (iv) Providing mission commandwith an operator control paradigm which lets them decide parameters defining the constraints andobjective functions of the associated constrained optimization problems. These should be exposed asmeaningful higher-level parameters as opposed to detailed low-level system parameter settings.

The first three items eventually enable the last. We also aim to extend the vision of the Joint Battle-space Infosphere to fully exploit the advanced capabilities of Semantic Web technologies for theknowledge-based information fusion. Current capability requirements demand the use of novelapproaches to data fusion and the technological platform of the Semantic Web provides a medium withinwhich many of these capabilities can be realized. This will allow for appropriate mediation of the levelswithin the JDL Data Fusion Model.

The relevance of this project to the US/UK military vision is in identifying where existingtechnologies need to be enhanced to meet the needs of the management of sensors in an ad hoc wirelessnetwork environment. In particular, we will develop methods and tools for setting constraints andworking regimes of a sensor network imposed by the network designer or mission commander, as well asfor exception handling if warfighter’s needs exceed the established constraints. The natural tendency ofwarfighters engaged in a battlefield is to request and maintain the highest possible quality of the resultsregardless of the long-term costs to the network (e.g., energy expenditure). Such immediate warfighter’sneeds must be balanced against the longer-term views of the network designers, deployers or higher levelcommanders that may have planned many missions in the area covered by any particular infrastructure. Amethodology for reconciling such contradicting needs would be an important challenge that we willaddress by developing methods and tools for maintaining constraints on the network use.

The research and technical development initiatives in semantically-mediated fusion will facilitateoperational effectiveness in coalition contexts. Firstly, the provision of a set of domain ontologies,subsuming different aspects of the coalition context, will provide an integrative framework for advancedsemantic integration and semantic interoperability. Such capabilities will contribute to improved sharedsituation awareness, a critical substrate for operationally effective decision-making. Secondly, we willdevelop tools and techniques to support collaboration and understanding. These capabilities will build onthe capacity for semantic interoperability and fusion to propitiate operationally-effective modes ofcollaboration, planning and decision-making. We shall also exploit knowledge-rich contingencies in theproblem domain, exposed by the ontology engineering and knowledge capture initiatives, to develop


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enhanced planning and decision support tools for commanders and force elements. Finally, thesemantically-mediated fusion approach will be delivered in the context of an extensible informationarchitecture that will facilitate the easy integration of additional service capabilities.


Task 2 is aligned with research in projects 1, 3, and 6 and Professor Szymanski will collaborate withthe researchers in those tasks on coordinating the work. Task 3 is aligned with research in project 12. Task4 is aligned with Professsor Shadbolt’s research in Project 12 with whom Professor Nixon maintains closecollaboration.


Prof. Nixon’s interests in semantically-mediated fusion stem largely from his interest in translatinggait as a biometric to surveillance capability by funding through the DTC Phase 2 cluster with Qinetic inNovel Biometrics. These are linked to Prof Shadbolt’s semantically mediated approaches which are anextension of extant UK DTC work which will be funded as a Phase 2 cluster project with Qinetic.


Research Milestones


Q1 Task 1 Draft of a report on the current literature and the proposed approachesto identification of the features and requirements of different types of sensorsfor unattended ground sensor networks (CUNY, US)

Q1 Task 2 Draft of a report on self-healing, self-optimizing sensor network routingalgorithms (RPI, US)

Q1 Task 3 Draft of a Report on tools and techniques to be used in theinvestigation of infrastructure management (IBM, UK; RPI, US)

Q1 Task 4 Draft of a Report on parameterization of trust, uncertainty,semantics and data fusion (Southampton, UK). An initialimplementation at IBM, UK (IBM, UK).

Q2 Task 1 Report on selection of the detection and tracking algorithms ofeach type of sensor, and cooperative processing algorithm (CUNY,US)

Q2 Task 2 Report on parameterization of routing algorithms (RPI, US)

Q2 Task 3 Report on representation of sensors and sensor fusion in an SOA(IBM, UK; RPI, US)

Q2 Task 4 Report on characterization of feature extraction, implementationof probabilistic approach (Southampton, UK)

Q3 Task 1 Report on design of multilevel data fusion algorithms formultimodal sensors, modeling and simulations (CUNY, US)

Q3 Task 2 Report on parameterization of outlier detection algorithms (RPI,US)

Q3 Task 3 Report on use of ontology’s in the semantic dynamic discovery ofsensor services (IBM, UK; RPI, US)


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Research Milestones


Q3 Task 4 Report on analysis and investigation of appropriate data,characterization of basic properties providing database for later use(Southampton, UK).

Q4 Task 1 Paper on energy-constrained multimodal data fusion forimproving target detection and tracking (CUNY, US)

Q4 Task 2 Paper on parameter selection in design and verification of sensornetwork algorithms (RPI, US)

Q4 Task 3 Paper on limitations of the SOA concept for managing distributedsensor networks (IBM, UK; RPI, US)

Q4 Task 4 Paper on instigation and evaluation of semantically mediatedapproach (Southampton, UK)


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13. Project 10: Mission Adaptive CollaborationProject Champion: Steven Poltrock, The Boeing Company



Steven Poltrock, The Boeing Company Winston Sieck, Klein Associates

Katia Sycara, CMU Michael Strub, ARL

Abbe Mowshowitz, CUNY Colin Cobridge, Dstl

Alun Preece, University of Aberdeen Gareth Conway, Dstl

Tim Norman, University of Aberdeen

John Allen, Honeywell

Simon Parsons, CUNY

Darren Shaw, IBM UK

Ian Hughes, IBM UK

Graham Bent, IBM UK


The long range objective of this research is to develop a theoretically grounded and empirically testedframework for agile, adaptive collaboration among humans and software agents engaged in collaborativedecision-making in time-pressured, high-stakes situations. Using this framework, people will be able toplan collaboratively, communicate while carrying out their planned activities, and adapt to changes inmission or circumstances. Elements of the framework will help them develop an effective plan, allocateand negotiate resources, dynamically revise their plans and resource allocations, coordinate theiractivities, and communicate with one another. The software that provides these and other services willunderstand the collaborative context, anticipate a user’s or a team’s needs, and potentially contribute to ateam’s activities in much the same way as a human participant.

Existing research has addressed many of the elements of this framework. Some research hasinvestigated human collaboration and how to support it, and other research has led to teams ofcollaborating agents. This research is unique in providing an integrated framework that supports hybridhuman and agent teams engaged in complex, planned collaborative activities. The research planned in thefirst year lays the foundation for development of this framework. The first year research issues include thefollowing:

A model for agents that contribute to team performance, adapt to human participants, and adapt tochanges in other environmental conditions

A methodology for agile construction, exploration, evaluation, and modification of collaborationprocesses

Collaboration mechanisms that decompose problems involving mission goals and enable humansand agents to coordinate, allocate resources, and negotiate while collaboratively achieving thesegoals.

Methods for enabling software agents to understand the collaboration context by interpreting theevents and information flow within the team.



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This project includes five interrelated research tasks, enumerated below:

Scenario development Models of hybrid human-agent teams Models of collaboration processes Collaboration mechanisms Models of information flow in social networksThis research is intended to support collaboration among human members of coalition forces, and the

first task grounds the research in that context. The project team will collaborate with Project 13 as theydevelop one or more scenarios that involve people from different cultural backgrounds working togetherin challenging circumstances. Project 10 will use these scenario(s) to identify research challenges andboth demonstrate and evaluate capabilities.

The communication and information technology services employed by coalition forces will beimplemented as distributed software agents, and these services will be most effective if they understandhuman intention, beliefs, preferences, processes, and the collaborative context. Task 1 will develop amodel of hybrid human-agent teams that supports collaboration among human and agents. This modelwill be strongly influenced by the scenario and will be tested by implementing components of thescenario.

When people collaborate they are guided by a plan or process that identifies a sequence of activities,who performs each activity, and the information that must pass between the people or agents carrying outthe activities. Task 2 will develop a methodology for agile construction, exploration, evaluation, andmodification of collaborative processes. These processes can serve as a plan for the hybrid human-agentteams addressed in Task 1. Constructing the process may involve selecting collaboration mechanisms(Task 3) that will be employed. The methodology will be validated by implementing models based on thescenarios defined by Project 13.

Task 3 provides the collaboration mechanisms and negotiation framework that enable agents toengage in collaborative activities with humans. Using these mechanisms, agents and humans will be ableto allocate resources, coordinate activities, and negotiate to resolve conflicts. Task 3 will develop aframework for formulating resource allocation problems and expressing allocation mechanisms. It willinvestigate how a software agent can represent commanders guidance regarding the intended solution ofsuch a problem and investigate methods for resolving conflicts or allocating resources by means ofnegotiation, with an emphasis on argumentation-based negotiation. This task will also investigatemechanisms for coordinating software agents and how these can be applied to hybrid human-agent teams.The collaboration mechanisms investigated will be chosen to address the resource constraints andcapabilities identified in the scenarios of Project 13. These collaboration mechanisms will serve as anintegral part of the hybrid team models developed by Task 1, and they will support the collaborativeprocesses of Task 2.

Task 4 investigates the flow of information within a social network and how this flow can beinterpreted to support enhanced collaboration within a hybrid human-agent team. The research willemploy text analysis and data mining techniques to extract meaning from conversations or larger units ofdiscourse. The software agents of Task 1 will use the observed flow patterns to understand and adapt tothe collaborative context. This task is linked to both Projects 11 and 12 because of overlapping staff,methods, and tools.

Task 1: Models of Hybrid Human-agent TeamsResearcher: Katia Sycara/CMU, Alun Preece & Timothy Norman/University of Aberdeen, John

Allen/Honeywell, and Simon Parsons/CUNY


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Both human collaboration and software agent collaboration have been thoroughly studied, but there isrelatively little research on hybrid human-agent teamwork [14]. Some research has identified the rolesthat agents could play in hybrid teams (e.g. supporting individual team members, being a teammate, orsupporting the team as a whole [12]). Some other work [6] has investigated trust concepts as thefundamental building block for effective human-agent teamwork, or posited the types of sharedknowledge that promote mutual understanding between cooperating humans and agents [1, 10]. However,many of the facets of human agent teamwork models, such as communication protocols for formingmutual intelligibility, performing team monitoring to assess progress, forming joint goals, addressing taskinterdependencies in hybrid teamwork, are still unexplored. Therefore, it is crucial to develop atheoretical model of hybrid teamwork that would be effective for the types of coordination envisioned inthe ITA research program. One goal of the model is to provide experimentally verifiable hypotheses.

Our approach in developing the model relies on interleaving two main steps: (a) an in depth study ofthe existing models of human-only teamwork, agent-only teamwork, and the limited prior work on hybridteams, and (b) studying the requirements for hybrid teamwork implied by the scenario(s) that will bedeveloped in Task 1. We will prepare a report of the findings of our study of the prior literature onteamwork models and revise this report as the study progresses.

We will determine characteristics of the scenario(s) that have important implications for the model ofhybrid teamwork. For example, the scenario may require asynchronous communication in a time stressedtask. A report will be prepared documenting these scenario characteristics and updated as scenariodevelopment proceeds. Concurrently, we will identify those features of the scenario that may differentiatedifferent teamwork models. This is important because different features will give rise to different researchissues. For example, if the scenario requires a centralized coordinator for the team, then the teamworkmodel should reflect this. This scenario-based requirement would give rise to additional research issues,such as how to manage bandwidth to communicate with the centralized coordinator. A report will describethese features and the research issues they raise.

We will begin developing a suitable model of hybrid teamwork that supports the scenario. This modelwill be based on our own previous research, the literature survey, and the requirements of the scenario.We will also identify dimensions that distinguish different hybrid team models and devise anexperimental design for evaluating alternatives. Conducting these experiments will depend on the state ofscenario development and the availability of testbeds for the experimental comparison.

Task 2: Models of Collaboration ProcessesResearcher: Steven Poltrock/Boeing and Darren Shaw, Ian Hughes, Graham Bent/IBM UK

When people work together they need a plan that describes how they will collaborate. This planincludes the sequence of activities they will collectively perform, who will perform each activity, andwhat information must be passed between people. This plan constitutes the process that they will followin carrying out their work, and generally people have an established, documented process for work that isimportant and done frequently. Of course, people must adapt these plans to the circumstances theyencounter, and when coalition forces work together, they are likely to find that they use somewhatdifferent processes and different ways of collaborating to accomplish the same work. This task willdevelop a methodology for agile construction, exploration, evaluation, and modification of collaborativeprocesses.

According to Coordination Theory [2] people must collaborate with one another when the workentails a constraint involving a shared resource, flow from one person (or entity) to another, or fittingtogether or integrating the results of different people or entities. In any complex human activity, all threetypes of constraint occur frequently, and that is why people work together. There are many ways ofsatisfying each type of constraint, and these methods are called coordination mechanisms. Because manycoordination mechanisms can satisfy the same constraint, there are many ways that people can worktogether to accomplish the same goals, although some ways are more effective than others.


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In collaboration with researchers at MIT we have developed a prototype tool for interactivelyconstructing process models, drawing on a knowledge base of coordination mechanisms. In this task wewill use this tool to model complex activities such as those defined in the scenarios developed by Project13. But first we will construct a model of a complex engineering process that we already know andunderstand as a means of gaining an understanding of the research challenges that confront us whenconstructing these models. Then we will construct models of activities described in the scenario.

We will investigate how choices of different coordination mechanisms yield different processes andhow we can evaluate the effectiveness of these alternatives. We will investigate how different processesfor accomplishing the same activity could be reconciled, just as a coalition might have to reconciledifferences in its processes. This research requires either identifying relevant coordination mechanisms inour existing knowledge base and integrating them into the process model or defining new mechanismsand adding them to the knowledge base.

Task 3: Collaboration MechanismsResearchers: Abbe Mowshowitz & Simon Parsons/CUNY, John Allen/Honeywell, Tim Norman &

Alun Preece/University of Aberdeen

Task 3 focuses on the framework and mechanisms needed by an agent system to support collaborationin ways that are useful and acceptable to its human members. In this research we will seek to (a) developa way of specifying and representing a problem together with the organizational and managerial elementsneeded to support a solution; (b) determine the properties or qualities of an acceptable solution orperformance of a task; (c) develop methods for coordinating the execution of complex tasks amongmultiple agents; and (d) determine how agents negotiate their roles in solving a problem or performing atask. In conjunction with the research performed in Task 1, this task will produce an agent communitywhose coordination and negotiation mechanisms and underlying structure are designed to support virtualhuman teams.

We will develop a framework based on the switching model of virtual organization [8, 9] in which toformulate the problem of resource allocation. Central to the switching model is the notion of a virtuallyorganized task, which consists of a set of abstract requirements, a set of concrete satisfiers, aspecification of the satisficing criteria (objectives) to be achieved in assigning satisfiers to requirements,and an algorithm or heuristic technique for making the assignment. The strict separation of requirementsfrom satisfiers provides the logical foundation for modeling resource allocation as a dynamic process inwhich assignment of satisfiers to requirements may change over time according to task objectives whichmay also change. The switching model allows all the components (i.e., requirements, satisfiers,assignment/re-assignment procedures, satisficing criteria, and task goals) that define a virtual organizationto vary over time. We will adapt the switching model of virtual organization to the needs of modeling themanagement of hybrid teams and represent collaboration mechanisms.

For an agent to be a useful member of a hybrid team, the agents must be able to understand what thesolution to a problem should look like. Leaders in hierarchical organizations often provide commandersguidance that specifies properties or constraints of a solution they expect subordinates to generate. In ahybrid team this translates into allowing humans to specify commanders guidance to agents and allowingagents to decompose and propagate appropriate potions of that guidance to agents solving sub portions ofthe problem.

We will analyze the scope and types of information used by mission planning commanders to specifythe properties and qualities of a preferred or acceptable plan. This will be done in collaboration with theProject 10 GTALs and will consist of the collection of either actual or exemplars of commandersguidance from past military exercises, war-games, or other sources. These exemplars will be analyzed andcharacterized to define the scope and extent of information typically used when specifying planningguidance. This work will be done in collaboration with Project 11 to understand potential cultural impactsto this information. Then we will design and implement a prototype system that addresses thehuman/agent interface for specifying commanders guidance. We propose to extend work done internally


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at Honeywell [3] on Policy to encompass both the rapid specification and representation of commandersguidance into a format that is understandable by both humans and agents. The prototype, in conjunctionwith the results of the commanders guidance analysis, will be used to design a set of experiments toevaluate the impact of the system on human-machine interaction.

Researchers in multi-agent systems have developed a wide range of computational mechanisms forcoordinating independent entities. These range from mechanisms in which agents send cues by modifyingtheir environments and so do not directly communicate, to mechanisms in which agents engage incomplex planning dialogues. The aim of this task is to establish the applicability of the range of availablemechanisms to the scenarios, and hence to help determine which forms of coordination might be bestemployed by hybrid teams under which circumstance, to help guide the decision of which mechanism willbe appropriate for a given team in a given situation. The work will begin by identifying a large range ofmechanisms from the multi-agent systems literature, and from this develop a state-of-the-art survey ofcoordination mechanisms, establishing, along the way, their applicability to the focus scenarios. Finally,this work will develop a formal ontology of coordination mechanisms in order to permit reasoning aboutthe suitability of different mechanisms in specific contexts.

Negotiation is important for the support of team formation and adaptation. Negotiation mechanismssupport team activities in two principal ways: (a) task allocation and reallocation as the situation unfolds;and (b) negotiation of the reassignment of resources and assets. We will investigate and assess existingmodels of negotiation with a particular emphasis on argumentation-based negotiation. Argumentation-based models offer a means by which standard negotiation mechanisms including auctions and bilateralbargaining may be enhanced to enable critiques of, and explanations for, offers and requests to becommunicated. In this manner, the system will foster shared understanding of the constraints on teamparticipants and maintain trust and task awareness. The critical survey of negotiation mechanisms will beinformed by the requirements that emerge from the scenarios to be developed by Project 13, and priorresearch in this field [4, 5, 7, 11, 13]. A specific approach for future research will be selected followingthis survey, and a prototype implementation will be developed by adapting an existing system. A specificscenario or vignette from Project 13 will be utilized to demonstrate this prototype.

Task 4: Models of Information Flow in Social Networks

Researchers: Darren Shaw, Ian Hughes, Graham Bent/IBM UK, Alun Preece/University of Aberdeen,and Steven Poltrock/Boeing

A team that is actively collaborating generates and exchanges information in a manner that is oftenhighly context dependent. Whilst the information is meaningful to human team members, it wouldgenerally be meaningless to a software agent that does not share the context. Task 4 will investigate waysof dynamically determining potentially meaningful properties of the events and information flow patternsin a collaborative networked team. A specific focus will be the way in which the interpretation of theinformation exchange is aided by utilizing existing information about the individuals involved in thecollaboration. This task will also investigate how software agents can use the same information todetermine their role in the collaborative task.

This research will initially be based on monitoring collaboration of teams using an instant messagingenvironment or a Metaverse environment (an emerging technology in which people interact and createcontent in a three-dimensional virtual environment, such as Second Life – http://www.secondlife.com).The instant messaging environment provides a useful proxy for the types of communication that couldoccur between collaborators in an ad-hoc wireless network (e.g. voice communication, text messagingetc) but in a textual form that can be automatically analyzed to determine information flow. The use of aMetaverse environment as a research vehicle can extend the concept to a richer environment includingspatial information. An existing IBM framework for studying instant messaging will be extended tofacilitate the use of text analysis and data mining techniques (associations and sequential pattern analysis)to identify the common threads and topics of conversation and the information flow structures that


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emerge within a collaborating team. This framework will then be further expanded to include monitoringof communication in a Metaverse environment.

Using the derived patterns of information flow, the task will investigate whether coherency ofcommunication patterns can be used to increase shared understanding between human and softwareagents. The objective will be to incorporate one or more software agents into a collaboration activity thatexplicitly make use of the pattern of communication to perform their tasks. This task will coordinate withProject 12, which will use similar text and data mining capabilities to support the advanced DCPDM.Research into the validity of Metaverse environments as an analogue of ‘real world’ collaborationenvironments will be undertaken in Project 11.


The goal of our research is to enable faster integration of the processes and procedures of differentcoalition members and to improve their coordination. The research will provide a method for explicitlyrepresenting and comparing collaborative processes that could be used by coalition forces to compare,integrate, and coordinate their planned processes. Conflict resolution, problem decomposition,negotiation, and resource allocation mechanisms will facilitate allocation of resources to tasks, rapid teamformation, and dynamic adjustments to allocations as circumstances change.

A hybrid human-agent team model provides a framework for all computing support of coalitionteams. Analyses of actual communication will enable the agents to understand the collaborative contextand provide assistance to coalition teams. Agents that monitor and support the teamwork can discover andpossibly repair flawed coordination in joint operations. Development of a theory of smart agents willimprove management of the information overload experienced by coalition war-fighters. Agents canfunction as filters of information and when combined with the technologies developed in Project 12, willlead to improved situational awareness.


This project will involve considerable collaboration among the project team members and with theother Technical Area 4 projects. This Technical Area plans to hold a workshop in September where allproject members (including ARL and MOD members) will work together on the development ofscenarios that capture key requirements of coalition forces and will drive our research supportingcollaboration. Sycara (CMU) has a part-time position at the University of Aberdeen, she will visitAberdeen in July, and she will begin working with Norman and Preece on the scenarios. The work onTask 3 will be performed collaboratively by Parsons and Mowshowitz (CUNY), Norman and Preece(Aberdeen), and Allen (Honeywell). To facilitate these collaborative relationships, Norman and Preeceplan to send a student and postdoc to visit their US collaborators. Steve Poltrock (Boeing) will work withthe IBM UK team to explore how their approach to analyzing communication patterns interrelates withmodels of collaboration processes. The IBM UK team will also collaborate with Alun Preece and TimNorman (Aberdeen) to conduct experiments with agents and humans engaging in information exchangeand coordination in the context of Task 4.

Steve Poltrock (Boeing) will collaborate with the Project 11 Project Champion, Winston Sieck, toexplore how cultural differences influence decisions about coordination mechanisms. The project teamwill also collaborate with Project 13 on the development of scenarios and Project 12 on the developmentof a range of ontologies.


Some of the proposed research is similar in intent to research in the ADA CTA. For example, recentadvances in the ADA CTA in the area of team adaptability modeling by Dr. Eduardo Salas and thescientists at UCF have resulted in a team adaptation approach. Adaptation is one of the objectives of thisproject, and CTA model may provide a useful framework for accomplishing this. ADA CTA research on


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Dynamic Network Analysis at CMU by Kathleen Carley, a colleague of Katia Sycara, is using socialnetwork analysis, which complements the approach planned in Task 4 of this project.

13.6. References

[1] Bradshaw, J.M., Acquisti, A., Allen, J., Breedy, M., Bunch, L., Chambers, N., Galescu, L., Goodrich,M., Jeffers, R., Johnson, M., Jung, H., Kulkarni, S., Lott, J., Olsen, D., Sierhuis, M., Suri, N., Taysom, W.,Tonti, G., Uszok, A, & van Hoof, R. Teamwork centered autonomy for extended human-agent interactionin space applications. In AAAI Spring Symposium. 2004. Stanford, CA.[2] Crowston, K & Osborn, C.S. A Coordination theory approach to process description and redesign. InT.W. Malone, K. Crowston, & G.A. Herman (Eds.), Organizing Business Knowledge, MIT Press:Cambridge, MA, 2003, pp. 335-370.[3] Dorneich, M. C., Whitlow, S. D., Miller C. A., Allen J. A. (2004) . A Superior Tool for AirlineOperations. In Ergonomics in Design. Spring 2004 vol 12. No. 2 pp. 18-23.[4] Karunatilake, N., Jennings, N. R., Rahwan, I. and Norman, T. J. (2004). Argument-based negotiationin a social context. In N. Maudet, P. Moraitis & I. Rahwan (eds), Argumentation in Multi-Agent SystemsII, volume 4049 of Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin, 2006.[5] Kraus, S., Sycara, K. and Evanchik, A. (1998). Argumentation in negotiation: A formal model andimplementation. Artificial Intelligence, 104(1-2):1-69.[6] Lenox T., Hahn, S., Lewis M., Payne T. and Sycara, K. Agent Based Aiding for Individual and TeamPlanning Tasks, IEA 2000/HFES 2000 Congress.[7] McBurney, P. and Parsons, S. A denotational semantics for deliberation dialogues, Third InternationalJoint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2004). New York City, NY,USA, 2004.[8] Mowshowitz, A. Virtual organization, Communications of the ACM, 40(9), 1997a, pp. 30-37.[9] Mowshowitz, A. and Kawaguchi, A. Quantifying the switching model of virtual organization. Journalof Information Technology Theory and Application, 6(4), 2005, pp. 53-74.[10] Okamoto S., Scerri P., Sycara K., Toward an Understanding of the Impact of Personal Assistants inHuman Organizations, Fifth International Conference on Autonomous Agents and Multi Agent Systems(AAMAS 06), Hakodate, Japan, May 9-12, 2006. (Winner of Best Student Paper Award)[11] Oren, N., Norman, T. J. and Preece, A. (2006). Arguing with confidential information. To appear inProceedings of the Seventeenth European Conference on Artificial Intelligence.[12] Stasser, G., Stewart, D.D., and Wittenbaum, G.M., Expert roles and information exchange duringdiscussion: the importance of knowing who knows what. Journal of Experimental Social Psychology,1995. 31: p. 244-265.[13] Sycara, K. Persuasive argumentation in negotiation. Theory and Decision, 28(3), 1990, 203-242.[14] Sycara, K., & Lewis M. Integrating intelligent agents into human teams, in Team cognition:Understanding the factors that drive process and performance., E. Salas and S.M. Fiore, Editors. 2004,American Psychological Association: Washington, DC. p. 203-231.


Research Milestones


Q1 Task 1 Interim report on an in-depth study of the teamwork literature - CMU

Q1 Task 2 Report on top-down development of a collaborative process model –Boeing

Q1 Task 3 Preliminary specification of an abstract framework based on theswitching model of virtual organization and a preliminary analysis ofexisting problem decomposition, coordination and negotiation


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Research Milestones


mechanisms. – CUNY, Honeywell and Aberdeen.

Q1 Task 4 Initial framework development - Report on the progress indevelopment of the research framework – IBM UK

Q2 Task 1 Final report on the in-depth study of the teamwork literature –CMU

Q2 Task 2 Report on a process model for activities described in the scenarios– Boeing

Q2 Task 3 Collaborative Walkthrough – a document outlining the interactionof the envisioned collaboration mechanisms on a simplified Task 1scenario. – CUNY, Honeywell, and Aberdeen.

Q2 Task 4 Framework Testing - Report describing the initial results obtainedusing the research framework and plans for initial experiments usingthe framework. – IBM UK

Q3 Task 1 Report on important features of the scenario including selection offeatures that would differentiate among potential different hybridteamwork models. – CMU

Q3 Task 2 Report on alternative process models created by selecting differentcoordination mechanisms – Boeing

Q3 Task 3 Evaluation plan – a description of how initial prototypes ofcollaboration mechanisms will be evaluated together with associatedmetrics. – CUNY, Honeywell, and Aberdeen.

Survey of problem decomposition, coordination and negotiationmechanisms in the context of virtual organization management and aspecific scenario. – CUNY, Honeywell, and Aberdeen.

Q3 Task 4 Human Collaboration Experiments - Report describing the initialresults obtained from the human collaboration experiments. – IBM UK

Software Agent Specification - specification of the agents requiredfor the Hybrid Collaboration Experiments. – IBM UK

Q4 Task 1 Report on the hybrid teamwork model. – CMU

Report on an experimental design for evaluating different featuresof the hybrid teamwork model using the scenario(s). – CMU

Q4 Task 2 Report on comparison and reconciliation of alternative processesfor accomplishing a common objective from the scenarios – Boeing

Q4 Task 3 Joint technical paper and prototype implementation ofcollaboration mechanism elements. – CUNY, Honeywell, & Aberdeen.

Q4 Task 4 Hybrid Collaboration Experiments - Report describing the initialresults obtained from the human/software agent collaborationexperiments. – IBM UK and University of Aberdeen


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14. Project 11: Command Process Transformation and Analysis

Project Champion: Winston Sieck, Klein Associates



Winston Sieck, Klein Associates TBA/RC Jitu Patel, DSTL

Gary Klein, Klein Associates Linda Pierce, ARL

Ian Whitworth, Cranfield DCMT Chatschik Bisdikian, IBM

Anne Kao, Boeing

Henning Schulzrinne, Columbia

Dave Roberts (IBM UK)

Ian Hughes (IBM UK)


This project focuses on developing an understanding of the command processes within coalitions aswell as the processes at work determining their external interactions. It will analyze the cognitive andsocio-cultural factors that facilitate or impair communication and understanding, particularly with respectto coalition planning teams. Furthermore, techniques will be developed for formally representing culturaland cognitive factors in ways that promote the transformation of coalition command processes whilepreserving their effectiveness and intent, and for establishing the basis for continuous monitoring of theirefficacy. The primary technical challenges in this work include the elicitation and analysis of culturalrepresentations, appropriate representations of coalition command concepts, approaches for coping withpatterns of conceptual differences across cultures, and automated assessment and monitoring ofperformance.

In the first year, the Project 11 team will research and develop rigorous analytical approaches forinvestigating and monitoring cultural, cognitive, and language phenomena with the aim of understandingand improving coalition planning and decision making. We will research theory and methodologies forcultural analysis, especially formal approaches for representing concepts and mental models that aresensitive enough to capture subtle cultural differences between cultural groups. We will develop acommand ontology to represent language patterns for US and UK coalitions. We will investigate meansto understand patterns of team communications and the content of communications in coalition commandprocesses, collaboratively with Project 10.

Finally, we will determine the feasibility of modeling and monitoring events in coalition commandprocesses. In addition, we will participate in three TA4 wide common tasks. In particular, the team willlead the first common task of a cross cutting human aspects activity in which the models and assumptionsabout human behavior in each of the projects will be reviewed.


In order to analyze and improve upon coalition command processes, several methodologies need to bedeveloped. In particular, we require techniques for modeling, measuring, and monitoring multinationalcommand processes. Models can be gleaned from existing single-service doctrine. However, the biggerchallenge is that the practices and procedures actually put into practice in any organization often differfrom those that are formally specified ([1-2]). This phenomenon is natural since command processes are


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often vague and subject to different human interpretations, as well as variances from establishedprocedures in response to the real cognitive challenges command staffs face. Therefore, methods formodeling aspects of command processes must draw on conceptions of experienced coalition commandersand their staff. Furthermore, transformation efforts must be augmented with mechanisms that can observethe processes that are followed on the ground.

Several technologies need to be developed to affect the transformation of coalition commandprocesses:

Studies of command processes need to be undertaken to understand cultural, cognitive, andlanguage issues that influence planning and decision making.

Formal representations of cultural, cognitive, and language factors need to be formulated. Techniques for using the representations to enable transformation of processes need to be

explored. Process monitoring techniques need to be developed. Tools which can automatically monitor the

command environment would be highly beneficial.

As part of the initial studies into the modeling and transformation of coalition command, we will befocusing on the first three aspects mentioned above. Thus, as part of our analysis, we plan to developmethods for modeling cultural differences in planning conceptions and mental models. We also intend todevelop language analysis techniques and create an ontology for US-UK coalition operations that willserve as the basis for improved force interoperation. This work would be done jointly with Project 12.Finally, automated performance monitoring tools will be developed in the form of a new flexibleinfrastructure for filtering and managing messages. This system will provide feedback on processeffectiveness.

Task 1: Cultural-Cognitive Analysis of Coalition PlanningResearchers: Winston Sieck, Klein Associates; Gary Klein, Klein Associates; Grad Student, Klein

Associates, Ian Whitworth, Cranfield

Cognitive architectures consist of at least two distinct knowledge systems. The first, explicitknowledge can be expressed with words or other symbols, and includes concepts, scripts, and schemata.Second, implicit or tacit knowledge, refers to the knowledge inherent in automated task performance,habitual tendencies and the like. Furthermore, domain-specific knowledge modules, such as the modulessupporting “collaboration” or “planning” consist of both explicit and implicit knowledge representations[6]. A fundamental assumption about culture, is that members of cultural groups share experiencesgrowing up in similar ecological and social contexts. These shared developmental experiences lead tomany important commonalities in the explicit and implicit representations comprising such cognitivemodules. Hence, understanding cultural differences in collaboration to support coalition commandprocess transformation requires the measurement and analysis of both kinds of knowledge.

The next breakthrough required in this space is the identification of differences in the distributions ofspecific aspects of mental representations. Accomplishing this will permit the development of culturalmodels of planning. The primary challenge is to find the means to appropriately elicit commander andsubordinate models of the conceptual structure of the command organization and processes. A secondchallenge is to explore statistical methods for analyzing the distributions of mental representations bothacross and within cultural groups (e.g. [4-5]). Known as “cultural consensus theory,” this collection ofanalytical methods has considerable potential over current approaches to analyzing cultures because itemphasizes domain-specific mental representations over general dimensions as the focus of analysis,providing greater opportunity to detect subtle cultural differences. Further, it treats within-culturevariability as a signal (rather than discarding it as noise), and allows for the classification ofrepresentations into culturally correct categories. Again, the overall aim of these approaches is to detect


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more subtle conceptual differences in planning and decision-making that exist between culturally similarnational partners than could be measured using extant approaches.

In the first year effort for Task 1, we will conduct a literature review on methodologies for mentalmodel elicitation and cultural consensus theory. We will then use these methods to elicit expert planners’(and/or or commanders) mental models of planning, including elements such as the roles and functions ofplans in addition to planning processes under various conditions such as in the presence of ill-definedgoals. Our initial hypothesis is that planning doctrine and official processes are misaligned with actualcognitive processing. Task 1 will include an initial pilot study to test that proposal, and additionally, toanalyze for differences between the US and UK experts’ mental models of planning.

Task 2: Command Language Analysis and Meta Language Development for Coalition ProcessesResearchers: Ian Whitworth, Cranfield; Post-Doc, Cranfield; Grad Student, Cranfield, Dave Roberts,

IBM

Command communications must be unambiguous and timely. It is therefore desirable to have somemeans of converting commands, orders, and instructions across internal coalition boundaries so that theirmeaning is maintained and the potential for ambiguities in understanding is minimized. Within coalitioncommand and control this commonly requires not only a reliable form of conversion from one set ofcommand protocols to another but also an ability to quickly perform that conversion to allow timelycommunications throughout all levels of command. Such a conversion requires knowledge not only ofdifferences in communication protocols within the coalition, but also the acceptability of forms of addresswith relation to existing differences in cultures and laws.

The first Task 2 activity will be to conduct a literature search on command process modeling. Thenwe will conduct research to elicit and represent a command ontology for US-UK coalition operations.Whilst single-service ontologies may already exist, it is believed that the construction of a coalitionontology will be a novel and useful undertaking. Derivation of a common command protocol, withcommon interpretation and understanding, will aid evaluation of the ‘agreed protocol’ approach, andprovide the basis for a command metalanguage to aid the ‘command conversion’ approach. It isproposed that a future demonstration of command conversion for this subset of commands, betweenvirtual organizations of limited size, structure and with small cultural differences might form a proof ofprinciple that command conversion is feasible. It is anticipated that further work will extend the commandconversion approach, which is likely to be favored by the customer because the approach will not impactexisting training requirements.

Task 3: Automated Discourse Analysis for Multinational CollaborationResearchers: Anne Kao/Boeing; Steve Poteet/Boeing; Advanced Technologists/Boeing, Dave

Roberts, IBM

The team would conduct research to understand the communication dynamics of multi-cultural teams.Existing techniques such as Latent Semantic Analysis (LSA) allow us to provide automated coding andmeasurement of semantic relatedness, and other contents and patterns of team communications incoalition command [3]. LSA approaches typically do not build a model of what is expected, but aremerely driven by the actual data. While this approach can serve as a good baseline, it fails to addressseveral key issues. A study of cultural differences requires a subtle understanding of the pragmatics ofspeech acts. Traditional information extraction techniques tend to focus on syntax and semantics. Whilesome of them include the use of a discourse model, subtle pragmatics are rarely addressed. For example,in some cultures and some situations it is not acceptable to explicitly disagree with a request. Otheraspects of speech acts such as how one requests information or clarification or how one agrees to a plancan vary subtly from culture to culture, and thus affect communication within cross-cultural groups.

One of the major attractions of LSA is that it does not require a knowledge base. However, in orderto get at subtle differences in language, domain knowledge and a certain level of language modeling areoften unavoidable. In our work we intend to leverage over already built ontologies, including WordNet,and any existing domain-specific ontologies (such as that developed in Task 2). In addition, we will also


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explore the use of ‘parallel corpus’ techniques to semi-automatically pick out potentially interestingcultural subtleties. For example, we will collect on-line headline news on the same story andautomatically pick out major variations in expression. These differences can be an indication of culturaldifferences in views, in styles of expression, or in culturally rooted allusions to historical events (e.g.“9/11” as a reference to particular event).

LSA is good at focusing on the overall themes and salient features and ignoring the ‘noise’ in the data,but it can potentially have the drawback of oversimplifying or glossing over important details. We intendto also explore text classification techniques (e.g. Naïve Bayesian, Support Vector Machines) inconjunction with the latest machine learning methods such as active learning and cost sensitive learning,to pick out minor or subtle differences in language, once we have established sample data using some ofthe above mentioned methods.

In summary, we intend to complete the following tasks in our three year study. (1) Construct a simpleknowledge base by leveraging existing ontologies, and by analyzing parallel corpora. (2) Establish sampledata and possible models for use by machine learning techniques. (3) Investigate various methods ofmeasuring cultural differences in language against sample data, using different parameters and models. Inyear 1 we will do an extensive literature search on existing methods and approaches to analyzing culturaldifferences, especially as applied to the language of cooperative tasks.

Task 4: Automated Monitoring and Instantiation of Command ProcessesResearchers: Henning Schulzrinne/Columbia; Graduate Student/Columbia, Dave Roberts, IBM-UK,

Chatschik Bisdikian, IBM-US

Realizing that unanticipated or else non-deterministic factors may affect the efficacy of commandprocesses, we plan to model and monitor the performance of such processes and provide feedback. Thereare two particular problems of interest to us. The first focuses on creating a flexible architecture that canbe used to monitor the execution of processes in distributed organizations with dynamic informationneeds. The second focuses on using this architecture to monitor the performance of command processeswith the goal of identifying execution issues such as errors, non-compliance, etc. Such performancemonitoring will be an ideal way to instrument and evaluate the tools and techniques to be researched inProject 12.

For the first research area, the team will research proposing a general architecture for flexible, secureand scalable presence and event notification systems that allows diverse users to be notified of events ofcurrent interest. This work will also feature in the tools to be developed in Project 12. Current presencesystems are rather limited in their capabilities and thus not suitable for larger, distributed organizationswith dynamic information needs. Events in the proposed system reflect status changes in field units,physical movements, supply chain events, physiological or mechanical events or availability forcommunication, which lends well to performance monitoring. The system encompasses a set of steps thatcompose and filter event notifications. The crucial research task is to automatically instantiate theseprocessing steps, so that they are transparent to users, but their behavior remains predictable.

In the first year, the Task 4 effort will involve a literature review of social networks and modeling,scalability limits and approaches for event notification and presence systems. We will then investigatecontent- and participant-based filters, as well as event types that are of special importance for militaryplanning and execution.

Task 5: Investigations into means to understand multinational communications andcollaborations

Researchers: Dave Roberts/IBM UK; Ian Hughes/IBM UK, Winston Sieck, Klein Associates, IanWhitworth, Cranfield.

There are two aspects to this task understanding the command process in the real world andunderstanding the applicability of using simulation environments as an analogue for representing realworld communication and collaboration.

It is critical that the data being gathered about command processes is validated with the real world.The most effective way to do this is to observe command situations and gather data (Klein, 1998).


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Because of the relatively high cost of this method, it must be carefully targeted. The studies will use amixture of structured and unstructured observations. If the first case this provides for the coverage of allof the areas of interest. In the later, this allows discovery of aspects that might not have been anticipated.These studies will be led by HF practitioners with significant experience of studies of this type. We willfirst search for information on previous studies of this type to ensure that we can build upon theexperience of others as well as the experience of the team. Secondly, we will plan a series of studies ofcommand processes in operation at events such as coalition exercises. (Note 1) A detailed plan will bedeveloped which includes the ways in which the studies both draw on and gather information for othertasks. For example, using preliminary results from task 1 as a structure for observations and then feedingthe results back to help development of the task 1 questionnaires. The plan will also allow for therefinement of methods, protocols and venues after experience has been gained during the initial studies.After each study, and more completely towards the end of the year, any information gathered will becorrelated with other tasks.

In addition to considering the nature of understanding the command process in the real world there isa need to research elements relating to emerging trends within large collaborative environments inemerging collaborative simulation Metaverse environments. The Metaverse is a term that has been used torepresent persistent online virtual worlds. These worlds typically allow the users from around the world tocreate their own function and space and allow that to be shared with a wider audience. In creating thisspace, which is typically represented as a 3d environment, a new set of interaction metaphors come intoplay. Investigations are required into other opportunities and problems associated with this sort ofenvironment as testbeds for emulating ‘real world’ multicultural collaboration. Much of the work in thisarea has been focused on game development, but this is has centralized control to craft the experience.The Metaverse and user created content is part of a trend in global, massive scale social interaction. Theseenvironments provide a completely free range user created experience. They are not placed as games, butas the future of the web experience, and human interaction in a connected environment. Further more,they allow us to collaborate with one another across language, organizational and cultural boundaries.Sharing a 3d persistent space allows for more expression and gesture in communicating ideas in a muchmore engaging way that video conference.

In the first year of Task 5, we will seek to understand the sociocultural and other human factorsassociated with the command process and how these can be represented in Metaverse environments. Wewill also explore the feasibility of the Metaverse to accomplish multicultural collaboration forexperimentation and “real world” task performance. The research objectives will map those cognitiveresearch projects that have taken place in both gaming platforms and high end virtual reality interactionssuch as CAVE (Cave Automated Virtual Environments) and evaluate their relevance to massively multi-user online systems.

Concepts that will be studied in the metaverse environment includes non-verbal communication andthe barriers/enhancements offered in a virtual environment. Non verbal communications offer anopportunity for potential measurement and investigation. It can be measured because of the electronicnature of the interaction. e.g. attending and sitting at meetings. It is possible to capture who sat where in avirtual meeting. What is not so easy is to capture and understand the dynamics of the team operating inthis space in a less formal way. So with human interactions, in a virtual environment, where theenvironment is significantly richer than the current electronic means of communication how do weinvestigate in order to extract meaning and improve the flow of information.

As far as barriers and enhancements are concerned, the rules of HCI change in a 3-d immersionenvironment. We are still bound by our standard devices and sensors. i.e. mouse and keyboard. We shouldnot ignore the expectation of 3d interaction, and the potential for effective communication in that space.We are a little way from a star trek holodeck, but it would appear that convincing telepresence isaccelerating from the less engaging and stilted phone conferences and video conferences. My experienceso far has been that manycommunication barriers are broken down much quicker due to the mix ofdistance in the form of an avatar, and engagement in the immersion in a irtual world.


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We will also contribute to effort in the cross cutting themes of TA 4 from this task to help establish arich understanding of the human aspects of our research throughout this project and projects 10 and 12.This will include reviewing a variety of human sciences methodologies and assessing their relevance andpotential contribution to our work.


Often, the commander of a coalition force is a commander in title only; mission accomplishment isachieved through coordination, communication, and consensus rather than by traditional commandconcepts, i.e., giving an order, (Joint Chiefs of Staff, 2000). Political and sovereignty issues makeplanning and decision making in coalition operations particularly difficult. To effectively coordinate andnegotiate consensus, commanders and planners must learn the cognitive and collaborative styles ofpartner nations or organizations, in addition to their capabilities and political goals. Also, the differencesin partners, languages, customs, equipment and training can cause several command communicationproblems. In this project, we will research and develop techniques for understanding the organization ofprocesses within multinational coalition environments, for transforming coalition command processeswhile preserving their effectiveness and intent, and for establishing the basis for continuous monitoring oftheir efficacy.


Intra-alliance collaborations, within and across technical areas, will be developed over the course ofthe first year. Special emphasis will be given towards the development of international collaborations. Tothat end, Klein Associates and Cranfield have held initial discussions concerning the feasibility ofengaging in staff rotation during the summer of 2008. Further collaboration opportunities will beidentified at the scheduled project meeting on 24-25 July 2006.


The cultural work is related to ongoing efforts within the ADA CTA. For example, assessment andfacilitation of multinational teamwork is an ongoing Klein Associates project effort in the CTA. The Task1 effort described in this IPP is distinctive in focusing on new approaches for cultural modeling based oncultural consensus theory, and by emphasis on upper echelon coalition planning teams. Thecommunication analysis techniques proposed in Task 3 are also related to ADA CTA efforts. However,whereas the existing CTA work is focused on LSA, the work here emphasizes other approaches foranalyzing communication streams that are capable of detecting more subtle linguistic phenomena. Thecommunication research is also being conducted by different organizations in Task 3 and in the CTA. Theother tasks do not have parallels to the CTAs/DTCs to our knowledge.

14.6. References

[1] Klein, G. (1998). Sources of Power: How people make decisions. MIT Press: MA.[2] Klein, G., & Miller, T. E. (1999). Distributed planning teams. International Journal of CognitiveErgonomics, 3, 203-222.[3] Landauer, T., Foltz, P., & Laham, D. (1998). An introduction to latent semantic analysis. DiscourseProcesses, 25, 259-284.[4] Romney, A. K., Batchelder, W. H., & Weller, S. C. (1987). Recent applications of cultural consensustheory. American Behavioral Scientist, 31, 163-177.[5] Romney, A. K. (1999). Cultural consensus as a statistical model. Current Anthropology, 40, 103-115.[6] Thuring, M., & Jungermann, H. (1986). Constructing and running mental models for inferences aboutthe future. In B. Brehmer, H. Jungermann, P. Lourens, and G. Sevon (Eds.), New Directions in Researchon Decision Making. Amsterdam: North-Holland, pp. 163-174.


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Research Milestones


Q1 Task 1 Post on Quickplace: Initial reference list of major previouswork on cultural consensus theory, related cultural analysismethods, and mental model/concept elicitation approaches.

24-25 July project team meeting. Post task presentations.

Q1 Task 2 Post initial reference list of major previous work on commandprocess modeling.


Q1 Task 3 Post initial reference list of previous work on issues, methods,and approaches for dynamic analysis of multiculturalcommunications.


Q1 Task 4 Post initial reference list of previous work on social networksand modeling, scalability limits and approaches for eventnotification and presence systems.


Q1 Task 5 24-25 July project team meeting. Post task presentations.

Q2 Task 1 Post draft review of existing work on cultural consensustheory and mental model elicitation methodologies.

Sept TA4 meeting

Post contribution to initial draft of BPP

Q2 Task 2 Post draft review of related work on command processmonitoring.

Report on consensus achieved with Nigel Shadbolt on project11/12 approaches to ontology representation.

Report on tool selected for ontology creation andrepresentation of command elicitation

Post draft questionnaires for ontology elicitation

Sept TA4 meeting


Q2 Task 3 Sept TA4 meeting


Q2 Task 4 Data sources identified for collaboration relationship and dataevent modeling

Post review of large-scale event notification systems


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Sept TA4 meeting


Q2 Task 5 Report on Metaverse environments as a viable analogue of‘real world’ collaborative interaction, with an emphasis onmultinational collaboration

Sept TA4 meeting.

Report on current thinking and best practice in designing andcreating simulation environments


Q3 Task 1 Report on completion of initial pilot data collection for 6-8expert planners or commanders

Q3 Task 2 Report on completion of initial data collection.

Q3 Task 3 Post draft review of issues and methods for analysis ofmulticultural communications.

Q3 Task 4 Report on acquisition of communication data.

Q3 Task 5 Report on relevance of Metaverse environments as a testplatform for multicultural collaboration and other ITA projectactivities

Q4 Task 1 Report on completion of data collection

Report on completion of data analysis of US and UK expertplanner mental models

Post task 1 sections of 1st year report

Post synthesized 1st year report

Q4 Task 2 Report on final data collection

Report on data analysis and creation of prototype ontology


Q4 Task 3 Post task 3 sections of 1st year report

Q4 Task 4 Report on initial design of event models and correspondingXML schema


Q4 Task 5 Post paper on the use of Metaverse environment’s forsimulating and analyzing military command processes



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15. Project 12: Shared Situation Awareness and the SemanticBattlespace Infosphere

Project Champion: Nigel Shadbolt, University of Southampton

Email: [email protected] Phone: +44 (0) 23 8059 3523


Nigel Shadbolt, University of Southampton Alun Preece, University of Aberdeen

James Hendler, University of Maryland Derek Sleeman, University of Aberdeen

Subash Shankar, CUNY Mark Nixon, University of Southampton

Emily Howard, Boeing Peter Waggett, IBM UK

Ali Bahrami, Boeing Peter Houghton (DSTL)

Graham Bent, IBM UK Cheryl Giammanco (ARL)

David Braines, IBM UK TBA/RC Jitu Patel, DSTL


Both the UK and US recognize the importance of trans-national alliances as the basis for futuremilitary operations. The vision is that military activities across all levels of the operational spectrum(from large-scale war-fighting to peace support and humanitarian assistance) will assume the form of‘coalitions of the willing’. Such coalitions will demand the close inter-operation, but not necessarilyintegration, of multi-national forces, each of which brings its own set of technological, ideological,organizational, procedural and cultural idiosyncrasies to the theatre of operations. Synergistic inter-operation between coalition elements in the future battlespace environment requires the availability of atechnological and knowledge infrastructure that is rooted in an understanding of the various informationmanagement and information exploitation challenges confronting military agencies in the 2020timeframe. The provision of such an infrastructure entails the successful resolution of a number ofscientific and technical challenges, including, but not necessarily limited to, the following:

Information Exchange: How can we support meaning-preserving modes of information exchangeacross organizational and cultural boundaries? The emphasis here is not so much on the technologicalbarriers to information exchange, so much as the ability to ensure that the semantics of informationcontent is not lost in information exchange contexts.

Shared Understanding: How can we promote a shared understanding of the temporal unfolding ofevents and information items within a particular situation? How can we support a common understandingof the semantic significance of information items, especially with respect to their semantic referents (thekinds of real-world objects represented by the information item) as well as their implications for bothcurrent and future strategic action?

Information Exploitation: How can we enable advanced modes of information exploitation thatcapitalize on the availability of advanced sensor systems and a global information space?

Shared Situation Awareness: How can we assess the contextual relevance of information, so as tooptimize information filtering and avoid information overload? What kinds of transformations arerequired at the presentational/visualization level to avoid asymmetries in situation awareness betweencoalition elements? Under what conditions do we want to maintain diverse viewpoints?

Communication & Collaboration: How can we enable a framework for the synergistic orchestrationof coalition elements at the strategic, tactical and operational levels? How can we integrate the results ofProjects 10 and 11 within this work?


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Decision Making: How can we enable the effective exploitation of decision-making processes? Howdo we ensure that such processes are sensitive to the differences between US and UK coalition elementswith respect to both command processes and the interpretation of decision outcomes?

Trust: How do we deal with trust issues in coalition contexts? What impact do such relationshipsimpose on coalition inter-operability? How do we foster trust between agents (both human and synthetic)in order to facilitate collaboration, communication and information exchange?


The technical approach for Project 12 is organized around 3 tasks which aim to address theaforementioned scientific and technical challenges. The approach adopted is grounded in the developmentof ontologies using subject matter experts (SMEs) and our extensive range of experience in humanknowledge elicitation [1]. A semantically-enriched Commander Information Model (CIM) for distributedcoalition environments (developed in Task 2) will provide a foundation for shared situation awareness;while a Commander Planning Model (CPM) (developed in Task 3) will assist in coalition planning anddecision making. The challenge of dealing effectively with the psychological and cultural differencesbetween military agencies in coalition contexts pervades all tasks within this project, and, as such, we willaim to align all research activities with the outcomes of the generic TA4 human aspects analysis initiative.The project will also exploit the scenario specification (developed in the context of Project 13) as a meansof scoping and grounding research activities. The provision of an ontological infrastructure for semanticintegration and inter-operability, in conjunction with the planning and decision support aids delivered inthe context of this project, will help to manage information overload, facilitate battle-space resolution andenhance the situation awareness of military personnel at all levels of the command hierarchy.

Task 1: Ontologies for DCPDMResearchers: Shadbolt (University of Southampton), Hendler (University of Maryland), Braines

(IBM), Shankar (CUNY)

The first task within Project 12 entails the development of a number of domain ontologies providing asemantically-enriched representation of the knowledge infrastructure associated with command processes(subsuming both planning and decision-making) in coalition contexts. These ontologies will support avariety of capabilities including, meaning-preserving modes of information exchange and semantically-mediated information fusion (supporting the work in Project 9). They will also provide a representationalsubstrate for planning and decision-making processes, aiming to model the conceptual infrastructureassociated with human problem-solving in these areas. The aim with regard to ontology engineering is notto provide rigid and inflexible command vocabularies, but rather conceptualizations that are able tochange and adapt to the evolving circumstances of ongoing operations. This is important because theeffort to develop a single monolithic ontology that encompasses the entire spectrum of humancompetence and expertise is untenable. Rather, we aim to provide multiple small-scale ontologies thattarget specific areas of domain-specific knowledge in ways that respect the culture-specific perspectivesof different agent groups. One of the distinctive characteristics of many extant semantic frameworksconcerns their inability to deal with multiple, small-scale, distributed ontologies - existing reasoningengines treat ontologies as monolithic entities, building a single integrated ontology rather than dealingeffectively with smaller, domain-specific fragments. This approach presents two major drawbacks: firstly,it does not scale well since the size of the ontologies to be integrated can be arbitrarily large, thuscompromising the ability of logical inference mechanisms to generate inference outcomes within anoperationally-useful timeframe; secondly, the reasoning processes to be deployed need to be sufficientlygeneral to cope with all the domain-specific ontology fragments that may need to be integrated in thecontext of some problem-solving task. A key research challenge therefore relates to the extent with whichontologies can be managed in a distributed fashion and integrated or aligned only on demand, somethingwhich is very likely to be the case in coalition contexts where team formation is highly dynamic


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(illustrated in Project 10). Ontology management and exploitation techniques will need to address thedevelopment of distributed reasoning techniques which allow reasoning services to be deployed inenvironments where domain knowledge is distributed across multiple, small-scale ontologies. Anotherresearch challenge concerns how far the emergent semantics4 of human practices - that change and evolvequickly - can be incorporated or else exploited in the context of conceptualizations that span military unitsand levels of command. Finally, it will be important to deal effectively with temporal information indistributed ontology environments. The key issue here relates to the development of suitablerepresentations of the temporal dimension as the basis for evaluating the relevance of extant informationand knowledge to ongoing decision-making and planning processes.

Since the ontology engineering in Project 12 countenances the notion of distributed, small-scaleontologies, it will be important to consider strategies for semantic integration and alignment. Ourintegration activities will focus on the exploitation of existing work at the University of Southampton inthe context of the CROSI initiative [2]. This initiative aims to facilitate the alignment and mapping ofknowledge elements across multiple, semantically heterogeneous sources. We will extend this work to themilitary domain by investigating whether it is possible to align diverse, domain-specific ontologies withrespect to culturally agnostic ontologies for coalition operations. Semantic integration technologies, inthis context, will provide support for the mapping, alignment and fusion of domain knowledge in adynamic, task-specific and problem-oriented manner.

Semantic integration efforts apply not just to the ontologies developed in the context of the currentinitiative, but also touch on extant approaches to coalition inter-operability, especially with respect toC2IEDM. In the context of the ontology engineering initiative for Project 12 we will build on existingwork with respect to ontological approaches to coalition inter-operability [3] and we will use experiencerelating to ontological formalizations of the C2IEDM at the University of Maryland and the ARL.

This task will act as a focal point for the cross cutting TA 4 theme Ontology Engineering – seeking tocoordinate issues of ontology development and the challenges associated with their use.

Task 2: Shared Situation Awareness & UnderstandingResearchers: Smart (University of Southampton), Howard (Boeing), Bahrami (Boeing), Bent (IBM)

Military operations in complex urban environments require commanders to consider the likelyoutcomes of their actions within a large multi-dimensional space, encompassing the political, economic,socio-cultural, legal, ethical and moral dimensions as well as the traditional military, physical andtechnical domains. Above all, it is vital that information is shared effectively across all lines of command,noting the specific potential of various intelligence sources to deliver important and timely information inparticular situation contexts. At all times, the effect of differences between coalition partners needs to beunderstood and factored into the strategies adopted for information exchange and informationvisualization/presentation.

To facilitate shared situation awareness in coalition contexts we will develop an underlyinginformation model (the Commanders Information Model [CIM]) that will allow us to show how theinfosphere of the future strategic battlespace can be exploited for the purposes of enhanced situationawareness. The notion of the CIM is grounded in an understanding of the cultural differences with respectto interpretation and understanding in information sharing contexts. It also encompasses the need toexplicitly represent information requirements in the context of various planning and decision-making

4 The notion of emergent semantics relates to the mechanism through which meaning arises out the interactionsbetween software agents, humans and metadata. One realization of this mechanism is provided by systems such asFlickr (http://www.flickr.com/) and del.icio.us (http://del.icio.us/). These systems provide users with a customizabletagging scheme that can lead to highly structured information sets. They provide a means for a community tocollaboratively build a taxonomy over time which is specifically suited for the data they share. This strategy avoidsthe upfront cost for agreeing upon a taxonomy when, perhaps, the nature of the information to be collected and itsuse are not yet known. It also allows the taxonomy to emerge and change dynamically as additional information isaccumulated. The products of this strategy have been termed folk taxonomies, or folksonomies.


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processes, such that available information can be selectively filtered with respect to an agent’s problem-solving specific needs and objectives (this is particularly important with regard to issues of informationoverload). In the future strategic battle-space environment the success of coalition interoperability will notsimply be predicated on the ability to exchange information across the various functional interfaces of theLand, Air and Maritime environments, neither will it consist in the integration of data from disparatetactical datalinks; rather, it will rely on the ability to provide a coherent semantic basis against whichculture-specific differences in the interpretation of various information sources can be resolved. This willrequire highly expressive mediums for semantic representation and disambiguation. The CIM is intendedto provide a realization of this capability. It assumes the form of an explicit, ontologically-circumscribedrepresentation of information requirements as encountered in selected planning and decision-makingcontexts. The aim is to exploit the semantically-enriched representations delivered by Task 1 for thepurposes of filtered views of the information space, views which are carefully aligned with theinformation and epistemic requirements of various problem-solving agents. In order to provide suchselective visualizations we will need to understand the differences with respect to informationexploitation and understanding in information sharing contexts. The key challenge here is to understandthe different meanings assigned to situation-relevant information items, not just in terms of theirextensional significance (e.g. their relationship to entities in the world), but also their implications foraction and decision-making. Since these interpretations are likely to differ across cultural boundaries, thework in this area will be heavily influenced by the outcomes of the generic TA4 human aspects analysisinitiative. In addition, the scope of the CIM will be determined by the scenario specification, delivered aspart of the scenario specification initiative in Project 13. The aim will be to support informationrequirements and information sharing in the context of the tasks and activities outlined in the scenario.

Enhanced situation awareness depends as much on an ability to assimilate information fromheterogeneous sources (both military and non-military) as it does on the semantic infrastructure tomanage this information and align it with information requirements. To facilitate the exploitation ofheterogeneous information sources we will make use of natural language understanding technologies toprovide flexible forms of information transformation. Our research in Year 1 will investigate techniquesfor extracting factual information from large volumes of text with a specific emphasis on thedisambiguation of ‘facts’, and the fusion of extracted information based on the different semanticcontexts in which the facts are discovered. Our ultimate goal is to determine how automatically extractedinformation about the same topic, extracted in different contexts, can be used to mediate the informationflow between different groups. In realizing this objective we will make use of UIMA (UnstructuredInformation Management Architecture) and existing techniques for extracting factual information fromunstructured text. We will also investigate the fusion of extracted factual information by extendingtechniques currently used for structured data fusion to combine extracted factual information on aparticular topic in different contexts. Specific ‘annotators’ will be developed to enrich the informationsubstrate for this process via the use of semantic annotations. Initially we propose to use ‘sentimentanalysis’ to identify sentiments that are expressed in texts and determine whether the expressions indicatepositive (favorable) or negative (unfavorable) opinions toward the subject. We propose to investigatehow the different ‘perceptions’ can be used to automatically mediate the transfer of information on thesame topic between contexts.

For coalition operations involving non-traditional information sources, visualizing information withina common goal framework will be essential to overcome many of the challenges associated with sharedunderstanding, situation awareness, orchestration, decision-making and trust. In designing visualizationsto support shared situation awareness, Endsley and her colleagues [4] have advocated a method todecompose a teams’ goals and decisions as a means of organizing required information. Using Endsley’sapproach, we will create a goal-directed hierarchy through interviews with consortium-provided SMEs inthe context of the ITA scenario (see Project 13). The resulting framework will provide the foundation forsubsequent visualization of the CIM.


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Commanders often have different roles, tasks/missions, goals and agendas, knowledge andbackground, personal/cultural preferences and access devices. They often need different pieces ofinformation, ranging from the real time information about a single building, to the resolution strategy of aglobal conflict. Sometimes, the time, location and past history of information access can also shape theinformation needs of users. Furthermore, information consumers, and information providers along withtheir capabilities may change dynamically over time and this could result in information overload. Wewill design a personalization framework [5, 6] that will be instantiated in the CIM by creating a genericmechanism that addresses the need for information aggregation in a dynamic, personalized, and process-driven way. Conceptual alternatives for integrating the personalization framework into the CIM and theCPM (see next section) will be generated in such a way that it supports real-time responses to change.The aim of this research is to develop an instantiation of the theoretical framework as well as theprototyping of basic services that can improve the quality of information for shared situation awareness,planning and decision making.

Shared situation awareness in coalition contexts depends on an understanding of the way in whichagents make sense of situations. To facilitate sense-making in coalition planning and decision-makingcontexts, we will develop collaborative sense making tools - such as those developed in the CoAKTinGproject [7]. Such tools will enable teams to make explicit the issues and rationale behind particularinterpretations of evidence and information, and we will investigate whether such techniques extenuatethe likelihood for misinterpretation and misunderstanding in collaborative decision-making and planningcontexts.

Task 3: Planning and Decision Making in Semantic Web Battlespace InfosphereResearchers: Smart (Southampton), Bent (IBM)

An important adjunct to shared situation awareness is the ability to identify decisive operational andtactical points at which the coalition needs to be working in synchronization to meet strategic objectives.These are also the points in the plan where the commander can assess a situation to determine if anyadjustments need to be made to ongoing operations, e.g. the deployment of reserves or of an AgileMission Group to provide additional momentum to the operation. We will exploit the work of Project 11in performance monitoring and process instantiation in this plan evaluation process; we will alsocapitalize on the work of Project 10 to determine what team elements needs to be updated or perhapsrepurposed. Our aim will be to develop a Commander Planning Model (CPM) which represents a task-oriented model of command processes in coalition contexts with opportunities for planning and decisionsupport aides clearly identified. The CPM will assume the form of an ontologically-motivated processmodel, detailing the tasks, agents and information items featured in a subset of coalition planningprocesses. In developing the CPM we will draw inspiration from existing methodologies for modeling thetask environment associated with knowledge-intensive domains, e.g. the CommonKADS methodology[8]. Initially, we will develop separate models for the UK and US and then use these as the basis foridentifying procedural and technological requirements for coalition inter-operability. The CPM will serveas the basis for knowledge service implementation, as well as the architectural framework for serviceinvocation and exploitation by coalition teams (comprising both human and synthetic agents) incollaborative planning processes. In developing the CPM we will need to define the scope of themodeling activity, and for this purpose we will rely on input from the scenario specification initiative (seeProject 13). The planning processes to be modeled and supported will reflect those featured in the ITAscenario.

The CPM will enable semi-automatic, mixed initiative planning processes. These will trace throughthe complex linkages between potential actions, in effect giving the commander a number of options andalternative CoA – with risks associated with each CoA. There are a number of approaches andtechnological solutions that could be usefully exploited here. Members of Project 12 have been involvedwith the O-Plan and I-X components used in the DARPA funded CoAX, CoSAR-TS and CoOPT projects.


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We will assess how easily these services could be provided as micro planning and decision aides in thecontext of the current task. We envisage a distributed architecture using web-based services that can beeasily disaggregated and reassembled as needed – example frameworks include MIAKT [9] which allowsservices to be easily integrated, and UIMA [10] which allows for the rapid integration of new informationtypes. Decision making aides, such as classifiers and model based qualitative reasoning, will also bereviewed for incorporation into the decision-making and planning solutions. The University of Marylandhas been developing a planning system, HTN-DL, which can directly integrate ontologies with AIplanning systems. We will explore how this model can work with the MIAKT and UIMA frameworks toprovide a mechanism for ontology-mediated coalition planning.

An important cognitive aspect of the commander’s decision-making and planning activities, is theneed to digest a great deal of, often uncertain, information and to devise strategies that take account of therisks associated with proposed Courses of Action (CoA) or variations on a proposed plan. As such, it isimportant to assess the trustworthiness of information obtained from different sources. We will investigateissues associated with trust in coalition contexts, particularly with respect to asymmetries in trustevaluation across the elements of a coalition formation. Issues of trust are also relevant to an evaluation ofinformation quality (Project 8) and we will aim to develop a common framework for the representation oftrust information across these projects (Project 12 and Project 8). We will draw on the generic TA4 humanaspects analysis initiative to facilitate an understanding of the cultural differences between US and UKforces with respect to trust evaluation, especially in terms of the potential asymmetries in trust associatedwith particular information sources, e.g. sensor systems.


Operational effectiveness in coalition environments is based on the need for inter-operability at avariety of levels. While inter-operability concerns are most easily thought of in terms of technology, it isimportant to remember that many of the most intractable problems lie in the realm of semantic inter-operability; we are not merely required to deal with issues of information exchange, so much asconsensual interpretations of the semantic significance of exchanged information. It is notable that despitesignificant overlaps between the UK and US at the cultural and linguistic level, important differencesremain and these may serve to undermine the effectiveness of information exchange as the basis forshared situation awareness and coalition planning. NEC will enable force elements to share informationpromoting both an understanding of the current operational context and the prevailing tactical situation.However, in order to be useful the information made available by network infrastructures needs to beinterpreted with respect to common semantic frames of reference. The aim is to develop a ‘commonunderstanding’ of the operational environment, not merely just a common visualization. If we are to trulyenable cross-service inter-operability, exploit the potential of NEC, and unleash the power the self-synchronization across force elements, we will need to focus on technologies that address the semanticambiguities inherent in both extant and future information systems. The issue of semantic integrationrequires an ability to establish mappings between the conceptual spaces adopted by different elements ofthe armed forces, e.g. across service components, between coalition allies and across military functions.Such integration can be facilitated by the exploitation of formal ontological characterizations of thedomain that make explicit the meanings of the conceptualizations exploited by various military and non-military agencies.

The research and technical development initiatives undertaken in Project 12 will facilitate operationaleffectiveness in coalition contexts. Firstly, the provision of a set of domain ontologies, subsumingdifferent aspects of the coalition context, will provide an integrative framework for advanced semanticintegration and semantic interoperability. Such frameworks will enable the exchange and integration ofinformation between coalition partners in ways that respect the meaning of information and the culture-specific perspectives of different coalition elements. Such capabilities will contribute to improved sharedsituation awareness, a critical substrate for operationally effective decision-making. Secondly, Project 12will develop tools and techniques to support collaboration and understanding between coalition partners.


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These capabilities will build on the capacity for semantic interoperability and shared situation awarenessin order to propitiate operationally-effective modes of collaboration, planning and decision-making.Thirdly, Project 12 will exploit knowledge-rich contingencies in the problem domain, exposed by theontology engineering and knowledge capture initiatives, to develop enhanced planning and decisionsupport tools for commanders and force elements. Finally, the technologies delivered by Project 12 are akey enabler for Effects-Based Operations (EBO) inasmuch as they endeavor to manage and exploit theexpanded information space associated with EBO. The notion of EBO is not necessarily restricted toweapons-centric thinking since effects will often be targeted against an adversary’s will to fight. Thetraditional notions of our kinetic weapons arsenal, as the primary means for targeting effects, will thusneed to be expanded to include the use of political and diplomatic intervention, economic sanctions andpsychological warfare tactics. The information space for EBO is therefore significantly greater than thattypically seen in past campaigns and is bounded by the 7 dimensions of the strategic environment for thefuture battlespace, the levels of command (Grand Strategic through to tactical) and the instruments ofnational power (Diplomatic, Economic and Military). An ability to deal effectively with the informationexploitation challenges demanded by EBO is therefore essential and will be augmented by the researchactivities seen throughout the current project.

Allied to the notion of EBO is the notion of Effects-Based Planning (EBP). EBP represents theplanning processes associated with EBO. As with EBO the emphasis is on the generation of operationalcourses of action that yield a range of physical and cognitive effects. EBP requires richer information thantraditional military planning processes. It relies on detailed knowledge bases to provide a thoroughunderstanding of the adversary’s culture and value sets, i.e. those values held by an individual, group,organization, regime or nation, which form the basis of their strategic CoG. It also involves anunderstanding of a potential adversary’s psychology, plus the formative factors (cultural, religious,ideological, historical, economical and political) that drive his intentions, objectives and modus operandi.EBP therefore entails a variety of information integration and semantic challenges. Information needs tobe acquired and assimilated from a variety of physically disparate information sources, considerably moreextensive than has heretofore been the case; it demands the interpretation of such information with respectto a common semantic frame of reference; and it relies on extensive background knowledge about how toco-relate potential opportunities for strategic action with available resources and legislative constraints.


The following intra-alliance collaborative opportunities have been identified in the context of Project12:

Project 9: semantically-mediated modes of information fusion; data quality management(especially with regard to trust and uncertainty).

Project 10: enhanced understanding of agents (competencies and capabilities); ontologicalcharacterizations of team members; an understanding of how situation awareness would need tobe shared amongst teams of various composition and grain size.

Project 11: cognitive + socio-cultural factors that facilitate or impair communication andunderstanding; an understanding of how information flows within and between commandstructures.

Project 6: application of trust models to the concept of risk in coalition environments. Project 8: implications of data quality for trust evaluation and the relative reliability of decision

outcomes.

A number of formal collaborative links need to be established with the DIF and HFI DTCs in the UK.We will establish such links by including research staff in both the ITA and DTC research programs. DrPaul Smart (University of Southampton) will participate in both the DTC and ITA initiatives.

We will facilitate collaborative processes in Project 12 by holding regular stakeholder meetings. Wewill also aim to exploit available technologies as a means of ensuring a common awareness of ongoing


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research and technology developments (discussion forums, message boards, teleconferencing, online chatfacilities, access grid, RSS feeds, etc). Some collaborative opportunities are facilitated by the co-locationof staff members at common institutions, e.g. stakeholders in Project 9 and 12 are co-located at theUniversity of Southampton. We will also capitalize on a number of existing bilateral relationshipsbetween Project 12 stakeholders, e.g. Hendler/Shadbolt, Shadbolt/Sleeman. No formal agreements haveyet been established regarding between Project 12 stakeholders regarding staff rotations.


A number of existing research programs are relevant to the current project. Where possible we willaim to capitalize on existing work by adapting or extending it to meet the unique challenges of the ITAinitiative.

The University of Southampton is currently undertaking research in the DIF DTC with respect to theapplication of semantic technologies to situation awareness in MOOTW contexts. There are manydifferences between this DTC project and the program of research advocated in the ITA. Thesedifferences have been discussed in the Fundamental Research Volume and elsewhere and will not bereiterated here.

Ongoing work with respect to C2IEDM is relevant to the work presented here, but differs in terms ofboth focus and objective. Firstly, C2IEDM is geared to the development of an information exchangemodel between military agencies; whereas military forces, throughout the 21st century, will frequentlyhave to work alongside diplomatic, humanitarian and civil authorities. Indeed, cooperation with non-governmental forces, private volunteer organizations and United Nations agencies has become the norm.Nevertheless, effective exchange of information between these groups and military forces in a bi-directional manner is still in its infancy. What is required in these cases, we argue, are flexible forms ofinformation transformation and visualization, underpinned by common frames of semantic reference.Secondly, the C2IEDM does not necessarily focus on meaning-preserving modes of information exchangein the face of the cultural, procedural, psychological and epistemic differences that may exist across theelements of a coalition force. Some initial work has been undertaken in this area using ontologicalapproaches, but further work is needed to extend the scope of such integration efforts and increase thesemantic expressivity of the representational formalisms employed.

The work of the CDMA, especially in relation to the Defence Data Repository (DDR), is relevant toour work and we intend to use this existing work as a point of departure in terms of the ontologyengineering effort. One challenge here is concerned with the alignment of existing data definitions (whichdo not avail themselves of semantically-enriched formalisms) with ontological characterizations (whichclearly do).

15.6. References

[1] Shadbolt, N.R. & O’Hara, K. (2004) AKT: Selected Papers 2004. ISBN 0854 328122.[2] Kalfoglou, T. & Hu, B. (2005) CROSI Mapping System (CMS): Results of the 2005 OntologyAlignment Contest. In Proceedings of the Integrating Ontologies workshop at the 3rd InternationalConference on Knowledge Capture. Oct 2-5, Banff, Canada.[3] Dorion, E., Matheus, C., & Kokar, M. (2005) Towards a Formal Ontology for Military CoalitionsOperations. Paper presented at the 10th International Command & Control Research and TechnologySymposium, McLean, Virginia.[4] Endsley, M. R., Bolte, B., and Jones, D. G. (2003) Designing for Situation Awareness. ISBN 0-748-40966-1[5] Bahrami, A. (2005) Achieving agile enterprise through integrated process management: from planningto work execution. International Journal of Cases on Electronic Commerce, October-December 2005,1(4), Pages 19-34.


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[6] Bahrami, A. et. al. (2006) The workflow based architecture for mobile information access inoccasionally connected computing. Paper presented at the IEEE International Conference on ServicesComputing (SCC 2006), September 18-22, Chicago, IL.[7] Bahler, M. et. al, (2003) Ontological Mediation of Meeting Structure: Argumentation, Annotation andNavigation. Paper presented at the First International Workshop on Hypermedia and the Semantic Web.[8] Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N. R., Van de Velde, W., et al.(2000). Knowledge Engineering and Management: The CommonKADS Methodology. Masachusetts:MIT Press.[9] Bontcheva, K. & Wilks, Y. (2004) Automatic Report Generation from Ontologies: The MIAKTApproach. Lecture Notes in Computer Science, Volume 3136, Pages 324 – 335.[10] Broder, A. Z. & Ciccolo, A. (2004) Towards the next generation of enterprise search technology.IBM Systems Journal, July 2004.


The research milestones for Project 12 are listed below. Deliverables (e.g. reports, research papersand technical solutions) are highlighted in bold font - primary responsibilities for the listed deliverablesare indicated in brackets.

Research Milestones


Q1 Task 1 Understand the knowledge and reasoning requirements for formalknowledge elicitation and modeling in both Project 12 and TA4 (TA4ontology engineering activity).

Identify source materials and subject matter experts for knowledgeacquisition (shared with Task 2).

Assist with TA4 human aspects initiative (TA4 human aspects activity).

Report - Review of relevant research and best practice with respect tothe management and real-time exploitation of ontologies in distributedcoalition environments. [IBM, UK; Southampton, UK; CUNY, US; UMD,US]

Q1 Task 2 Review and understand issues associated with shared situation awarenessin coalition contexts.

Identify coalition subject matter experts (shared with Task 1).

Examine and identify issues associated with information personalizationin coalition contexts.

Assist with scenario specification activities (Project 13 scenariospecification activity).

Report - Natural Language Processing (NLP) techniques for factextraction and semantic annotation. [IBM, UK]

Q1 Task 3 Investigate and review trust issues in coalition contexts.

Report - Parameterization of trust, uncertainty, semantics and data fusion(joint deliverable with Project 9). [Southampton, UK; IBM, UK]

Q2 Task 1 Acquire relevant domain knowledge from available knowledge sources,including subject matter experts (shared with Task 2).

Develop formal domain ontologies for Project 12 and TA4 (TA4


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Research Milestones


ontology engineering activity).

Evaluate existing standards, tools and techniques used to manage andquery ontologies, and perform a gap analysis on these to determine how bestto enable effective ontology management and exploitation in distributedenvironments.

Report - Proposed changes/additions to standards, tools and techniquesto enable effective ontology management and exploitation in distributedenvironments. [IBM, UK; Southampton, UK; UMD, US]

Q2 Task 2 Acquire relevant domain knowledge from available knowledge sources,including subject matter experts (shared with Task 1).

Report - Information personalization for shared situation awareness.[Boeing, US]

Develop Commander’s Information Model.

Report - Initial results on the fusion of factual information fromunstructured text. [IBM, UK]

Q2 Task 3 Develop Commander Planning Model for Semantic BattlespaceInfosphere.

Q3 Task 1 Develop initial framework for ontology management in distributedenvironments – ontology mapping and alignment solution using militarysensor network scenario (data acquired from Project 8).

Technical Solution - Domain ontologies for coalition planning anddecision-making. [Southampton, UK; UMD, US]

Q3 Task 2 Create Goal-Directed Hierarchy framework

Extend the Goal-Directed Hierarchy framework to include aspects ofadaptive information aggregation to support personalization.

Report - Initial results on semantic annotation and the extraction of factsin different semantic contexts. [IBM, UK]

Q3 Task 3 Report - Commander Planning Model [Southampton, UK; IBM, UK]

Q4 Task 1 Technical Solution - Knowledge portal featuring knowledge repositoryand knowledge accessibility services. [Southampton, UK]

Paper - Semantic alignment and integration strategies for coalition inter-operability. [IBM, UK; Southampton, UK; UMD, US; CUNY, US]

Paper - Instigation and evaluation of semantically mediated approach(joint deliverable with Project 9). [Southampton, UK]

Continue development of framework for ontology management andsemantic integration in distributed environments.

Q4 Task 2 Develop initial technical solutions for shared situation awareness andsense-making.

Paper - Knowledge-mediated strategies for shared situation awareness incoalition contexts. [Southampton, UK; Boeing, US]

Create preliminary conceptual alternatives for information


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Research Milestones


personalization as part of preliminary versions of CIM and CPM.

Paper - Fusion of information in semantic context and its relevance tothe mediation of information transformation. [IBM, UK]

Q4 Task 3 Develop initial service-oriented framework for decision support services– discovery, invocation, exploitation.

Paper - Ontologically-motivated characterization of command process incoalition planning contexts. [Southampton, UK]

Report - Impact of trust model assumptions to the acceptance of decisionoutcomes and inter-agent knowledge transfer in coalition planning contexts.[Southampton, UK]


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16. Project 13: Cross-Project ActivitiesProject Champion: Dinesh Verma, IBM



Peter Waggett, IBM UK Jack Lemon, Dstl

Parviz Kermani, IBM US Jitu Patel, Dstl

Bob Rosenthal, IBM UK John Gowens, ARL

Brian Rivera, ARL Greg Cirincione, ARL

Gavin Lock, Logica Gavin Peterson, Dstl

16.1. Project Summary/Research Objectives

This project covers the various activities that need to be conducted across several projects, and are ofa non-administrative nature. Participation in these tasks will be from all the alliance members, and thenames listed explicitly are for people who will be facilitating and coordinating the activity.

In the first year of the ITA program, we envision the following tasks to be conducted across thedifferent technical areas in the first year.

The first task deals with the development of a scenario that can be used to drive the requirements ofdifferent research projects over the course of the program. The scenario will be updated and modified asappropriate for the project.

The second task deals with obtaining and maintaining datasets that can be used to address theresearch needs of various projects during the course of the program. Information datasets are required forvalidation of algorithms and hypothesis in many projects, and several projects may need the same type ofdata-sets.


Task 1: Development of the ITA ScenarioParticipants: Dinesh Verma, Gavin Pearson, Peter Waggett, Gavin Lock, Logica, Brian Rivera, ARL,

Gavin Peterson, Dstl, plus collaboration from all alliance members

The goal of this task is to develop a scenario that can be used to drive the requirements of researchtask being undertaken in ITA. The scenario will be created to emulate a realistic environment and acontext for the research projects and tasks. The scenario will also be used to validate the results that areobtained from various tasks in the alliance, and one can assess the increase in the effectiveness of militaryoperations in the scenario when the results of the research activities are applied to the scenarioThescenario development will be based on existing knowledge-base and environments such as Binni(http://www.binni.org) which is further described in white papers such as TTCP C3I - CSA White PaperNo. 4 v0.2. After taking an inventory of the existing scenarios and contexts, we will determine thoseaspects required for ITA research that may be missing from the existing scenarios, addressing questionssuch as (i) do the scenarios capture contexts such as urban warfare and three-block war (ii) are thescenarios detailed enough to drive technical research issues and requirements (iii) are the scenarioscovering diverse aspects of coalition operations, etc. We would extend the existing scenarios so that theappropriate issues are captured appropriately.

Task 2: Collection of the ITA DataSetsParticipants: Parviz Kermani, Dinesh Verma, Peter Waggett, Collaboration from all alliance members

http://www.binni.org/


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The goal of this task is to identify and collect appropriate data sets for networking, security, sensorinformation processing, and decision making which can be used for validation and testing of varioushypothesis associated with the projects conducted in the course of the alliance. Information about theavailable datasets and downloadable versions, where appropriate, will be posted onto the website of thealliance.

16.3. Technical Deliverables

Research Milestones


Q2 Task 1 Compile and Review existing scenarios. Identify gaps andrequisite enhancements.

Q3 Task 1 Report on the scenario developed for ITA operations.

Q2 Task 2 Compile and Review existing datasets

Q3 Task 2 Provide web-page with information about available datasetsfor alliance.


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17. Schedule of Meetings and ConferencesIn order to promote collaboration and cross-fertilization of ideas across the project teams, several

meetings will be held during the course of the year. The schedule of the planned meetings and theirlocations are described in the table below.

Dates Event Location of Event Purpose

June 7-9, 2006 Technical LeadersWorkshop

Hursley, UK Develop the technicalvision and broad projectdirections.

June 12-14, 2006 Technical Workshop Cambridge, UK Team building amongthe alliance members.

Finalize contents oftechnical work for thefirst year.

July 17, 2006 CMC Meeting Teleconference First Quarter CMCMeeting

Review the IPP for thealliance.

July 26, 2006 TA-3 Meeting Hawthorne, NY, USA TA 3 Meeting andProject 8 Face to Face.

September 11-13 2006 TA 4 Meeting Hawthorne, NY, USA TA 4 Face to Face

September 18 2006 Formal Program Launch Palisades, NY, USA

Belfont Lake, UK.

Formal launch of theITA program

September 19-20 2006 TA-1 Meeting Hawthorne, NY, USA TA-1 Project LeadersMeeting.

November 27 2006 CMC Meeting Teleconference Second Quarter CMCMeeting

November 2006 TA 2 Meeting Hawthorne, NY, USA TA 2 Face to Face

February 26 2007 CMC Meeting Teleconference Third Quarter CMCMeeting

March 11-13, 2007 Annual Conference College Park, MD Annual Conference

Fourth Quarter CMCMeeting,

July 16-20, 2007 Summer Camp Troy, NY Summer Camp.

Documents

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