Addressing Limitations of Tactical Networks by Estimating ... · • Some disagreement among researchers (which is healthy!) • Discuss approaches to determine QoI and VoI –Consider

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UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces

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Addressing Limitations of Tactical

Networks by Estimating and Exploiting

Information Quality and

Information Value

Niranjan Suri Senior Research Scientist – Florida Institute for Human & Machine Cognition (IHMC)

Visiting Scientist – US Army Research Laboratory

UNCLASSIFIED

UNCLASSIFIED The Nation’s Premier Laboratory for Land Forces

• Old Adage – Getting the Right Information to the Right People at the

Right Time

– Or – put another way – Getting Actionable Information to Soldiers at the

Right Time

– Objective has not changed

• What has changed?

– Increased deployment of sensors (with increasing resolution, coverage,

persistence)

– Increased access to open-source and social media data

• What has (unfortunately) not changed?

– Tactical networking environments still limited in terms of bandwidth,

latency, reliability, stability, and connectivity

Motivation

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• Need to move away from the (mostly) current paradigm:

– Gather everything we can

– Shove what we gather into the network

– Live with what we get out of the network

• Quality of Information (QoI) and Value of Information (VoI) promise to

be new metrics that can help

– QoI and VoI can be used to filter and prioritize transmitted information

– For example

• Do not transmit low quality information (exceptions?)

• Do not transmit information with little or no value

• Another benefit: filtering unnecessary information can reduce the

cognitive workload on the Soldier

New Measures and Metrics

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• Attempt to define QoI and VoI as metrics

• Some disagreement among researchers (which is healthy!)

• Discuss approaches to determine QoI and VoI

– Consider different types of information – video, pictures, audio, sensor

reports, tracks, documents, maps, etc.

• Discuss applications of these concepts to the DoD missions

• Consider different perspectives

• Analyst, Commander, Operator, others?

• How would we experiment with these concepts and measure

improvements?

• Are there other metrics / measures worth including?

• For example – Quality of Experience (QoE)?

Panel Objectives

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Lance Kaplan is a Scientist in the Networked Sensing and Fusion

Branch, Sensors and Electron Devices Directorate (SEDD), at the US

Army Research Laboratory (ARL). At ARL, Dr. Kaplan is serving as the

Government Technical Area Lead for the Information Network Academic

Research Center, which is a multi-institutional basic research program

aimed at advancing fundamental understanding of information networks in

conjunction with their interactions with social/cognitive and communication

networks. Dr. Kaplan has published over 180 technical articles. His

current research interests include signal and image processing,

information/data fusion, resource management, and network science.

Panelists (1)

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Michael Kolodny has over 50 years of R&D engineering and

management experience at The Army Research Laboratory (ARL) / Harry

Diamond Laboratories. He retired in May 2009 and is currently serving as

a Senior Technology Consultant for ARL. He has been very proactive in

autonomous networked sensors and unattended ground sensor (UGS)

activities within DoD. He founded and chaired the RDECOM UGS IPT

and has been working actively with DoD in defining technology programs

to support the Army and other DoD organization’s autonomous sensing

needs in various programs. Mr. Kolodny’s most recent efforts have been

focused on new technology concepts including interoperability of ISR

assets. He founded the initial UGS Standards Working Group that has

now been chartered by USD(I) and is chaired by DIA. Mr. Kolodny

established and chairs the Conference on “Ground/Air Multi-sensor

Interoperability, Integration & Networking for Persistent ISR” which is part

of the annual SPIE DSS Symposium.

Panelists (2)

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Mark Linderman is a Principal Researcher in Information

Management Technologies in the Air Force Research Laboratory

Information Directorate where he leads the Information Management

technology area. From 2000-2004, Dr. Linderman was the Technical Lead

of the AFRL Information Directorate Joint Battlespace Infosphere (JBI)

Program. The JBI program addresses a broad spectrum of information

management challenges from access control, the role of metadata,

dissemination, persistence and destruction of information. Dr. Linderman

joined AFRL in 1994 after completing his M.Eng. and Ph.D. in electrical

engineering from Cornell University. He holds a BSEE degree from the

University of Delaware.

Panelists (3)

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Derya Cansever is the Chief Engineer of the Communication

Networks and Networking Division at US Army CERDEC, where he

conducts research in Tactical, Mission Aware and Software Defined

Networks. Prior to CERDEC, Dr. Cansever worked at Johns Hopkins

University Applied Physics Laboratory, AT&T Bell Labs, and GTE

Laboratory. He taught courses on Data Communications and Network

Security at Boston University and University of Massachusetts. He has a

Ph.D. in Electrical and Computer Engineering from the University of

Illinois at Urbana Champaign.

Panelists (4)

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Sastry Kompella received the Ph.D. degree in electrical and

computer engineering from the Virginia Tech, Blacksburg, VA, in 2006.

Currently, he is the Head of the Wireless Networks Research Section

under the Information Technology Division, U.S. Naval Research

Laboratory, Washington, DC. His research interests include various

aspects of wireless networks, from mobile ad hoc to underwater acoustic

networks, with specific focus toward the development of cognitive,

cooperative network optimization techniques for efficient spectrum and

other wireless resource allocations.

Panelists (5)

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Begin with each panelist presenting opening remarks (10-15 minutes)

Open the floor for discussion

Audience – please make note of questions to ask the panel!

– Otherwise, I have to resort to my canned questions

Potential Next Step (after Panel) – Edited Book

– Provided there is sufficient interest!

– Combination of invited and peer-reviewed chapters

– If you are interested, please get in touch with me

Panel Format

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Paper on VoI in October IEEE Communications Magazine

Shameless Plug

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Panel Members

• Lance Kaplan

– US Army Research Laboratory

• Michael Kolodny

– US Army Research Laboratory

• Mark Linderman

– US Air Force Research Laboratory

• Derya Cansever

– US Army CERDEC

• Sastry Kompella

– US Naval Research Laboratory

• Make note of your questions – or email them to me at

[email protected]

Let’s Get Started!

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Backup Slides

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• Our approach

• Discretize information into Information Objects

• Determine value of information (for each IO)

• Use value estimation to prioritize and filter information

• Enable receiver and context-dependent delivery

• Maximize available network capacity

• Reduce operator data overload

• Question – how to determine right / actionable information?

Value of Information

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• Many definitions in the literature for Value of Information

• Game Theory – Price someone would pay for information before making a

decision

• Information Value Theory – Value obtained by a reduction in uncertainty /

increase in profit

• Our definitions:

• Information Object (IO): A discrete piece of information (e.g., a picture, a

sensor report, a document, a video clip, a track)

• Value of Information (VoI): A numerical value between 0 and 1 assigned to

an IO

• VoI of 0 implies it is of no value

• VoI of 1 implies it is of maximal value

• VoI of an IO is relative to a recipient (e.g., a soldier)

• Different from Quality of Information (QoI), which is intrinsic to the IO

Defining Value of Information

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• Assumptions:

– Number of Information Objects (IOs) exceed network’s ability to transport

and deliver all of the information

– Delivering all of the IOs available overloads the soldier

• Determining the VoI for each IO allows us to sort the IOs in decreasing

order by value

• Once sorted, algorithm can

– Send the most important IOs first, maximizing available network capacity

• If capacity is reduced, lower rated IOs are dynamically skipped

– Filter out IOs whose computed VoI is below a desirable threshold

• Reduce overload on soldier

How is VoI Useful?

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IO’s delivered to

applications

such as ATAK

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Value of Information (2)

Question – How to determine value of information?

Abstract (working) definition – information is of value if it causes the receiver to improve his/her situation awareness and/or course of action

What is a (reasonably) computable approach to solving this question?

Would need to take into account many factors – Current mission, current activity, current location

– Mission Information Exchange Requirements (IER)

– Predicted location / activity in time (brings in temporal dimension)

– Type of information, source, pedigree, recency

– Quality of Information

– Vetting / validation of the information (sometimes, receiver dependent)

– Range of influence

– Information known a priori

– Receiver’s preferences

– Receiver’s social network and trust relationships

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Proactive Dissemination Service (DSPro)

– Model mission (including routes, objectives, roles)

– Match available and incoming information against mission model

– Prioritize information dissemination based on relevance estimation

– Pre-stage information for edge users to increase availability

– Learn user preferences over time

– Realize a flexible push/pull architecture

Realization

Operations Center

Information Valuator

NodeContext 1

NodeContext n

IO

IO

IO

Tactical Network

Tactical Network

User 1

User n

Operations CenterTactical Network


Sensor Network


IO

IO

IO

Analyst 1

Analyst 2

(a) From OC to Edge Network (a) From Edge Network to OC (c) At the Edge Network

Tactical Network

User 1User 3

Sensor Network

User 2

User n

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Modeled Metadata – Georeference / Spatial Extents

– Timestamp

– Expiration Time

– Source / Pedigree

– Type

– Classification

– Relevant Missions

– Priority

– Source Reliability

– Information Content

Realization (2)

User Context – Initial representation

includes: – Identity

– Location

– Mission Name

– Task / Role

– Projected Path(s)

– Relevant Distance

• By Type of Object

– Selection Threshold

– Weights for Ranking

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)( lietd wl)w(i+)w(e+)w(t+)w(dVoI •d : distance from the route / position •t : expected time of use •e : expiration time •i : importance •l : learned preference •w : corresponding weight factor

00.20.40.60.8

1Proximity

Time to Use

ValidityImportance

Preference

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• Range of Influence

• History of Previously Delivered Information and Change Delta

• Account for Value Added by new Information Object

• Object Size

Enhancements

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• Estimate the Range of Influence of Tracks based on type (MIL2525

standard symbol)

• Configurable policy specifies range of influence for different

symbol patterns

• Policies can depend on attributes such as

– Friendly, unknown, enemy

– Type of entity – airborne, ground, sea

Range of Influence

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Matchmaking History used in conjunction with distance to compute

relevance of subsequent updates:

A track that moves 100 m, but is 1 km away, is assigned a higher

relevance than a track that moves 100 m, but is 10 km away.

History and Change Delta

Matchmaker

Object Metadata Node Context

Area of RelevanceMil STD 2552 Type

Client-Assigned Importance...

Node PathNode LocationNode Mission

Node Role...

MatchmakingHistory

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Multiple Smaller Objects Vs Fewer Big Objects?

– Prioritization by Relevance/Object size ratio

– Filtering of undeliverable data

Object Size

Matchmaker

Object Metadata Node Context

Area of RelevanceMil STD 2552 Type

Client-Assigned Importance...

Node PathNode LocationNode Mission

Node Role...

MatchmakingHistory

Network Topology

Link capacityLink delay

...

Scheduler

Multiple Tracks

~ 15 KB

Single Image

~ 150 KB

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Scenario: Information Dissemination to the Tactical Edge

Scenario includes dissemination of tracks and sensor reports to

dismounted soldiers

Soldiers can choose between three levels of selectivity

– Low (most inclusive), Medium, high (most selective)

Initial Results (1)

SATCOM

Tactical Operations Center

SATCOM SATCOM

MANET MANET

MANET

SATCOM

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Initial Results (2)

0

100

200

300

400

500

600

700

800

900

1000

SATCOM MANET

Ban

dw

idth

Uti

lizati

on

(K

B/s

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) (L

ow

er

is B

ett

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Increasing Information Selectivity --->

DSPro Performance Improvement

Baseline

Low - Naïve

Low - DSPro

Medium - Naïve

Medium - DSPro

High - Naïve

High - DSPro

Performance Improvement

26.85% to 45.39% (SATCOM)

31.16% to 64.86% (MANET)

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Control

Control the number and type of ISR assets

deployed / tasked to support mission requirements

Drive/steer ISR collection using existing, deployed

assets, control what is gathered to support mission

requirements as opposed to gathering everything

Integrate C2 requirements of multiple missions

Shaping of data: downsample imagery,

audio, video, summarize documents

Based on mission requirements

and network state

Prioritize based on type; e.g. type

of traffic – VoIP

Way Forward

Mission based Collection

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Broader Context

Agile Computing Middleware Realizes Many Protocols for Discovery,

Transport, Dissemination, Information Management

Building the OODA Loop for the Network

Source code for Mockets, DisService, NetProxy has been released

under open source GPLv3 license

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Wired / Wireless Network

Agile Computing Middleware

Mockets

Group

Manager

(Discovery)

AgServe

ApplicationApplication

Dissemination

Service

UDP

DSPro

ACM

Network

Proxy

Serial

Representing

Commander’s

Intent

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Interconnected Puzzle

Network Awareness

Tactical Communications Network

Opportunistic Resource

Exploitation

Quality of Service and

Prioritization

Intelligent Dissemination

Sensor Tasking

Mission and Information

Requirements

Software Agentsfor Offloading TasksMoving Computation

to Data

Resource Discovery

Valueof

Information

Tactical Clouds

Information Ranking and

Filtering

Anomaly Detection

Confidence Computation and Actuation

Wearable Computing and Quantified Self

Data Shaping

Resource Coordination and

Allocation

Mediating Between Multiple Information

Systems

Data Analyzers / Aggregators

Social Network Analysis / Mining

Documents

Addressing Limitations of Tactical Networks by Estimating ... · • Some disagreement among researchers (which is healthy!) • Discuss approaches to determine QoI and VoI –Consider