Integrating the self-growing concept in a self-organizing wireless network for topology optimization

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENTInt. J. Network Mgmt 2014; 24: 121–152Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/nem.1856

Integrating the self-growing concept in a self-organizing wirelessnetwork for topology optimization

Apostolos Kousaridas,1,*† Alexandros Kaloxylos,1,2 Panagis Magdalinos,1

Thanos Makris,1 Georgios Koudouridis,3 Gunnar Hedby3 and Nancy Alonistioti1

1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece2Department of Informatics and Telecommunications, University of Peloponnese, Karaiskaki Area—St George Park,

Tripoli 22100, Greece3Radio Network Technology Research, Huawei Technologies Sweden AB—R&D Centre, Stockholm, Sweden

SUMMARY

The concept of self-organizing networks is considered one of the most promising approaches for the efficientmanagement of future wireless networks that will support a large number of nodes and a plethora of serviceswith diverse characteristics. Today, different types of networks (e.g. WLANs, wireless sensor networks) aredeployed to serve different needs but do not interoperate. Their possible loose integration will provideopportunities that could be exploited through collaborative approaches to devise novel solutions to extendthe capabilities and improve the performance of these networks. The self-growing paradigm addresses thischallenge by extending network nodes to dynamically evolve in terms of purpose and operational features.In this paper we describe the CONSERN architecture, which targets the realization of the self-growing conceptin the context of self-organized networks. To test our ideas we designed and implemented a WLAN topologyoptimization scheme that provides the best coverage at a minimum energy consumption, through dynamicaccess point (AP) deactivation and reactivation. Using self-growing mechanisms and typical motion detectorswe present how the operation of the proposed topology optimization mechanism can be improved. The reducedenergy consumption attained under the proposed scheme at the AP side, as well as the efficient utilization ofnetwork resources, are evaluated via a proof-of-concept implementation that we have deployed in a real officeenvironment that consists of WLAN APs and motion sensors. Copyright © 2014 John Wiley & Sons, Ltd.

Received 21 December 2012; Revised 5 December 2013; Accepted 10 February 2014

1. INTRODUCTION

Over the past decade, extensive efforts have been undertaken in the area of autonomic networkmanagement. These efforts try to address the increasing complexity that derives from the interactionof a large number of network devices and target the need to reduce human intervention [1]. The goalis to avoid the static and predefined parametrization of network protocols that constitute a bottleneckfor the control and management operations [2,3]. The adoption of self-organizing network (SON)concepts and mechanisms is considered one of the most promising approaches for the managementof networks that operate in highly dynamic and dense environments [4]. The main functionalities in aSON consist of: (a) self-configuration for automated set-up and configuration of nodes; (b) self-healingfor fast autonomous failure mitigation; and (c) self-optimization for real-time network optimization [5].The importance of all these functionalities for next-generation networks (also commonly referred asself-x mechanisms) has already been acknowledged by standrdization bodies and international researchfora that have provided the first technical specifications [5,6].

*Correspondence to: Apostolos Kousaridas, Department of Informatics and Telecommunications, National andKapodistrian University of Athens, Athens, Greece.†E-mail: akousar@di.uoa.gr

122 A. KOUSARIDAS ET AL.

SON mechanisms, up to now, have been considered for single-purpose networks (e.g. managingcellular telecommunication networks). However, a very recent concept is taking advantage of thecombination of different-purpose networks that are deployed at the same location. This concept iscalled ‘self-growing’. A self-growing network is defined as ‘a novel type of network composed of(heterogeneous) network nodes and sub-networks that can cooperate and utilize their reconfigurationcapacity to optimize on-demand for a dedicated (temporary) purpose, also augmenting capacity byassociating with additional nodes, networks, services and functions in that’ [7]. A self-growingnetwork exploits the coexistence of different types of networks deployed in the same geographical areaand might serve diverse user needs (e.g. private indoor wireless networks, wireless sensor networks(WSNs) for a variety of tasks from security to logistics and environmental control). Until now, thesenetworks have operated in a stovepipe way since they do not interact at all. However, as we willdemonstrate in this paper, even a limited cooperation among these heterogeneous networks mayimprove the robustness or the performance of existing network management mechanisms.In this paper we present in detail the CONSERN architecture [8], which targets the realization of the

self-growing concept in data networks, where heterogeneous networks’ capabilities (e.g. WSN) aredynamically exploited and combined, optimizing data networks’ autonomic operations. To validatethe viability of this architecture we implemented it along with a SON mechanism for WLAN topologyoptimization that optimizes energy consumption. The mechanism allows the dynamic deactivation orreactivation of IEEE 802.11 access points (APs), based on network density and monitored trafficconditions. The results indicate a 20% saving in energy consumption. Furthermore, in order to testthe self-growing attributes of the architecture, we experimented with the integration of motiondetection sensors that operate in the topology under study and are used to provide spatial/geographicalinformation. This information is used on the fly, exploiting self-growing solutions, to evolve thedecision-making engine of the topology optimization algorithm. The updated scheme provides betterdecisions for topology optimization while keeping the energy-saving gains at the same levels.The remainder of the paper is structured as follows. The background work for autonomic network

management, the self-growing concept and SON mechanisms for topology optimization in wirelessnetworks are presented in Section 2. The functional architecture for the development of cognitiveself-growing networks is described in Section 3. After that, we present the topology optimizationscheme, which exploits the functional architecture and self-growing principles. Next, the deploy-ment of the proposed architecture and the instantiation of the proposed solution through a test casein a realistic office environment are described. Finally, we conclude the paper and sketch futureresearch directions.

2. BACKGROUND WORK

This section presents the state of the art in the areas of autonomic network management and topologyoptimization mechanisms for energy saving. Our aim is to discuss how the concept of self-growing,which is a newly defined SON mechanism, can improve the operation of specific mechanisms suchas those dealing with coverage and capacity under energy-saving constraints.

2.1. Autonomic network management and self-growing networks

Managing Future Internet networks is going to be a very tedious task. This is because of the increasednumber of devices that will have to be managed, as well as their heterogeneous nature. This is whyresearchers are trying to find ways to reduce the required human intervention in fundamental managementprocedures. New mechanisms have been designed to render network components capable of configuring,optimizing, healing and protecting themselves. This autonomic behaviour of the nodes imposes aparadigm shift from following a set of a priori agreed requirements to the empowerment of nodes todynamically adapt to changing network conditions [1,9]. The majority of existing proposals in theresearch literature for autonomic systems are based on so-called closed control. Each autonomic elementconsists of an autonomic manager and the respective managed resource [10]. Every autonomic manager,

123INTEGRATING SELF-GROWING CONCEPT IN A SON FOR TOPOLOGY OPTIMIZATION

based on knowledge about the system configuration, monitors and analyses the status of an element,decides upon possible remedy actions and executes them.Designing an autonomic network management system involves various technologies and disciplines

and has received significant research effort during the last decade. ETSI AFI has recently launched thespecification of autonomic management systems [11]. 3GPP introduced the concept of SON to reducethe operational expenditure associated with the management of network components for tasks such asnetwork planning, configuration and optimization [5]. Several architectures for autonomic networkmanagement have been recently proposed, e.g. FOCALE, INM, AutoI, SOCRATES and, morerecently, UMF. These architectures, which have been extensively reviewed [12–14], converge to acommon agreement on the major functionalities that are required for the specification of an autonomicnetwork management system. Context awareness, the existence of a knowledge plane, policy-baseddecision making and network operator governance block constitute the main common componentsof these architectures.The above-mentioned architectures focus only on single-purpose networks (e.g. managing cellular

communication networks). The self-growing concept extends the notion of autonomic networkmanagement by providing the capacity to dynamically evolve the purpose and the functionalbehaviour of an entity. This is achieved through collaboration and interaction among heterogeneousentitites. A self-growing network is considered to be a novel SON concept and it is purpose driven.Self-growing entities are able to follow a predetermined path in their functional evolution and theircapability to achieve multiple purposes along this path. It is utilizing state-of-the-art concepts andenablers to realize this evolution, such as node and network reconfigurability, cognitive decisionmaking and self-learning capacity. In contrast to existing approaches for autonomous networks, theevolution of a self-growing network follows some rules of ascending complexity along its life cycleregarding reconfiguration and collaboration capacities. Thus a self-growing network cannot freelyevolve uncontrollably but is restricted to evolve towards an intended purpose. Nevertheless, the degreeof freedom to deviate from a planned life cycle is related to the purpose of a self-growing network. Inthis scope, the optimal balance between the autonomic and cooperative paradigms may differaccording to the purpose of the self-growing network. This is reflected in the rules that govern theevolution of the network, favouring (and motivating) varying degrees of cooperation between thenetwork elements. The following key elements define the self-growing attribute:

• A life cycle is defined as either a self-determined or a pre-planned path along a sequence of pro-gression points that define (potentially temporary) stable points in the evolution of a self-growingnetwork. Progression points can be associated with stable configurations of a network potentiallyproviding different functionalities.

• A progression point will associate with a set of attributes. These attributes can each be describedby a non-empty set of parameters. If a set of metrics is made available for these parameters, theprogression point is measurable. An associated descriptive set of factors (i.e. values of parame-ters) then makes a progression point well defined.

The sequence of progression points defines the life cycle of a self-growing network and the set ofrules defines how it evolves through this life cycle. A well-defined and measurable progression pointmay associate with a dedicated purpose of the network. This property of a self-growing network al-lows factorizing the transition between distinct purposes of the network. Accordingly, comparingthe values of metrics associated with adjacent progression points provides a way to define and measurethe cost or benefit of a transition between purposes. For a number of scenarios, a life cycle may forktowards multiple potential target purposes. This is especially true for event-triggered progressions,where the type of event determines the next purpose to enter (e.g. in an emergency situation).

2.2. Topology optimization for energy saving in wireless networks

Energy efficiency has been extensively studied in specific research areas, e.g. wireless sensor net-works, where sensors’ battery life is a crucial parameter for WSN duration [15]. In recent years, energyefficiency has been a fundamental concern for almost every ICT field (e.g. wireless networks [16]).Various methods for the reduction of energy consumption in wireless communication systems have

been proposed. Samdanis et al. [17] present two algorithms for energy saving in radio accessnetworks, based on the concept of energy partitions. These are associations of powered-on and -offbase stations formed by a collective decision among network elements. The objective is to matchthe overall offered bandwidth in terms of coverage and capacity in a dynamic manner. The simulationresults suggest that generally centralized algorithms perform better especially as bandwidth demandper user increases because it is easier and more effective to shift active sessions among cells at once.Also, in Auer et al. [18] the problem of minimizing the power consumption of wireless accessnetworks by switching on and off and adjusting the emitted power of base stations is presented. Thedecisions for these actions are based on different traffic profiles, and an integer linear programmingmodel is introduced that allows selecting consumption while guaranteeing coverage and capacity foractive users in a service area. Moreover, in Richter et al. [19] the authors propose deploymentstrategies on the power consumption of mobile radio by exploiting micro base stations per cell inaddition to conventional macro sites; the metric of area power consumption as a system performancemetric is introduced. In a full traffic load scenario, the use of micro base stations has a rather moderateeffect on the area power consumption of a cellular network. Cao et al. [20] consider multi-BScooperation and wireless relaying technologies and specifically how the energy-saving performanceis affected by system parameters such as traffic intensity and network density. From the aboveanalysis, it is obvious that various solutions have been proposed to tackle the problem of wirelessnetwork topology optimization combined with energy saving.We claim that the integration of the self-growing concept in a SON could extend the performance

management capabilities of a communication network. For instance, several approaches have beenproposed for the reduction of energy consumption in WSNs or in individual components of wirelessnetworks. In our work we exploit capabilities and information that a WSN provides for a specific typeof service, so as to improve the performance of a WLAN (energy saving, coverage), which is adifferent purpose. As presented in the following sections, a self-growing network exploits thecoexistence of different types of networks (e.g. WLAN and WSNs) that initially do not interoperateto devise novel performance management mechanisms.

3. ARCHITECTURAL FRAMEWORK FOR SELF-GROWING COMMUNICATIONNETWORKS

In the context of the EU FP7 CONSERN project 16 use cases have been defined [21,22] that impose self-growing requirements by networks operating in home and office environments. These use cases includeenergy optimization in an office environment under coverage contraints, energy-aware end-to-end delayoptimizations, purpose-driven network reconfiguration during an emergency and dynamic meeting set-upin a flexible office/building environment. The use cases were analysed and a number of functional andnon-functional requirements were extracted. Then, by carefully following well-established softwareengineering methodologies [23–25], we identified APIs for the building blocks and formulated therequired message sequence charts that instantiate all the use cases. Finally, an excerpt of this work hasbeen implemented and a particular use case (topology optimization in a WLAN environment) was putto real-life experimentation. After completing the design of the architecture we followed a backwardtraceability process to make sure that the initial requirements, described in the use cases, were coveredby the functional blocks.As mentioned before, the proposed architecture, entitled CONSERN, targets the realization of the

self-growing concept in data networks, where heterogeneous networks’ capabilities (e.g. WSN) aredynamically exploited and combined, optimizing data networks’ autonomic operations and tasks. In thecurrent work, we adopt the principles of research efforts in the area of autonomic network management[26,27]. Most of them are based on the control loop concept (Figure 1).The CONSERN architecture assumes the existence of typical IP nodes (ranging from sensor devices

to IP routers) that can be found in a home or in a business environment. The main entity introduced isthe self-growing cognitive manager (SCM). SCM, depicted in Figure 2, is a software entity embeddedin IP nodes in order to perform self-x actions (i.e. optimization, configuration, healing and growing).

Figure 1. Autonomic control loops/processes

Self-Growing Cognitive Manager (SCM)

CONSERN Cognitive Engine (CCE)

CommunicationServices

Translation

Monitoring

Execution

Learning

Decision Making

AutonomicControl

Cooperation

Self-Growing

Information BaseUser

ProfileServiceProfile

PolicyBase

KnowledgeBase

NodeProfile

Figure 2. The self-growing cognitive manager

A CONSERN-enabled network contains one or more SCMs that are able to communicate with eachother as well as with other components with networking capabilities (e.g. sensors) or even legacycomponents. Additionally, a CONSERN-enabled network is capable of communicating with adjacentCONSERN-enabled networks, thus highlighting the full extent of application of self-growing networks.Each SCM realizes a control loop consisting of four steps: (a) monitoring, (b) decision making, (c)

execution of decided actions and (d) learning from prior decisions and execution results. Every controlloop can be executed between different SCMs, each of which may provide a subset of the requiredfunctionality. Following this distributed approach, the CONSERN architecture envisages the executionof multiple coordinated cognition loops across different SCMs that may operate at different timescales.This way, the network is able to identify and react to events caused by both fast- and slow-changingnetwork dynamics. The overall CONSERN architecture described below consists of two parts: (a) thefully functional SCM; and (b) the interfaces for the network administrator side.

3.1. The self-growing cognitive manager

A fully functional SCM is composed of three main components: (i) the CONSERN cognitive engine(CCE); (ii) the communication services: and (iii) the translation (TRA) function.

3.1.1. CONSERN cognitive engineCCE provides the intelligence enabling the SCM to realize the self-growing paradigm, cooperationand energy-efficient mechanisms developed in the context of the project. The CCE is not an ‘atomic’function but it is composed of lower-layer functions each of which is responsible for executing specificparts of the cognition loop. A detailed analysis of those functions is presented below.

Information base (IB). The IB contains information about the state of the network. The data stored in IBcan be classified into five logical groups, adopting the approach that has been proposed in ASA [28]. ASAprovided a proposal for the automated management of both Internet services and their underlying networkresources. In the context of CONSERN architecture we have extended the node profile (i.e. ResourceInformation Base of ASA) and the knowledge base (i.e., Knowledge Information Base of ASA), focusingon the dynamic behaviour of network nodes in order to support the self-growing concept:

• User profile: contains information related to users, such as personal information and subscriptions.• Service profile: contains information about the status and requirements of active services, such asparties involved, required resources and billing.

• Node profile: contains information about the static and dynamic profile of the node. The staticprofile includes the hardware specifications of the node, initial set-up, etc. The dynamic profileincludes current resource utilization, state of the node, etc. The node profile also containsinformation on all possible configuration alternatives of a node, e.g. the existence of an SDRfunctionality in the node that enables the node to potentially switch from one radio technologyto another.

• Policy base: contains high-level policies specified by the network operator/administrator in theCONSERN policy manager entity.

• Knowledge base: contains information about events (triggers), actions and the results of theactions. Knowledge can be initially provided by the operator/administrator and is thereafter builtthrough the learning procedure that is executed at various timescales, e.g., each time an event isobserved its corresponding actions and their results are stored. Several tools can be used forknowledge building; starting from simple statistical models (e.g. distance, variance) andextending to association rule mining (e.g. A-priori) or even reinforcement learning techniquessuch as Q-Learning [29]. The author should be cautious at this point; the task of building theknowledge and the associated models is performed by the learning component, while theknowledge base simply stores the outcome. The use of the knowledge base is to enable a nodeto perform a certain action upon the triggering of a certain event without the need to executethe control loops in the CCE (i.e. find actions that were applied in similar situations). Thisreduces the processing requirements in the nodes while in parallel increasing the intelligencelevel in the network. The knowledge base is composed of three interacting layers:

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- Self-growing knowledge: Knowledge related to the self-growing algorithms is stored in thislayer. Usually it is of high-level scope, in the sense that it describes joint actions from mul-tiple nodes of the network.

- Cooperative knowledge: Information related to actions and events regarding the self-organizationof the network. It can also describe joint actions from multiple nodes of the network.

- Autonomic control knowledge: This holds specific triggers and actions on node level. This isthe absolute least required knowledge in order for a node to be able to operate with somelevel of autonomicity.

Decision making. The decision-making function has an interface with the monitoring function toextract the current state of the node as well as the state of the neighbouring environment. Afterdeciding on specific actions, it communicates with the execution function in order to enforce thedecisions. In addition, it is connected with the learning function to provide information about the

decisions undertaken. Using this information the learning function may create knowledge that can beused to improve the operation of the decision-making function in the future. Finally, an interfacebetween the decision-making and communication services functions is specified so as to enablecommunication of the decision-making function with other CONSERN functional units, as presentedin Figure 3. The decision-making function is composed of three separate functions that operate in afully distributed manner: (a) autonomic control; (b) cooperation; and (c) self-growing. Note that allnodes do not need to support and operate all three functions. This depends on the type and role ofthe node. These decision functions are described below:

• Autonomic control (AUC): The AUC function provides node-related decisions in the sense thatno information exchange with the environment is needed/performed. Configuration actions affectonly the node that currently hosts the SCM (e.g. monitoring periodicity).

• Cooperation (COP): The COP function makes decisions that require the cooperation of othernodes (e.g. cooperative channel reselection). It can operate at a higher level than the AUC functionsince it makes more complex decisions that affect a number of nodes. It can also control theoperation of the AUC function so as to avoid any conflicting decisions among them.

• Self-growing (SGN): The SGN function realizes the self-growing paradigm. It holds the statemachines of the potential growth of the network towards certain reconfigurations. It communicateswith the COP and AUC functions in order to exchange information for decision making either at theself-growing level or at the self-x level or at the autonomous node level. Another functionalityprovided is the verification/validation of the configuration actions executed in the past. Specifically,in the occurrence of an event that causes the execution of the SCM, specific configuration actions aregenerated and applied to one or more network elements.

For all three of these functions, an evaluation of their operation is performed by checking the datacollected in the IB after their execution. For example, for the SGN function, the network state shouldbe checked in order to verify that the undertaken actions were executed correctly and the behaviour ofthe network is the one anticipated.A high-level view of the COP and the AUC functionality is illustrated in Figure 4. Their decision-

making engine follows an autonomic and/or a cooperative approach. It includes the problem-solvingtechniques for network nodes’ efficient adaptation. Thus it uses the developed knowledge model aswell as situation awareness mechanisms. The first stage (i.e. symptom identification) includes theidentification of faults or optimization opportunities, building situation awareness from raw monitoringdata (e.g. channel utilization, packet error rate). Parameters are correlated, analysed and filtered out in

CONSERNConfigurable

Gateway (CCG)

CONSERNSensor

Coordinator(CSC)

Functional Unit

Sensor DataAggregator

Functional UnitManagedResource

CONSERNPolicy Manager

Functional Unit

Policy Provider

SCM-CPM

SCM-CCG

SCM-CSC

Self-GrowingCognitive

Manager (SCM)

SCM-SCM

CSC-SDA

CPM-PP

CPM-CSC

CPM-CCG

Legacy Network

Self-growing Network

Sensor Network

Self-Growing Cognitive Manager(SCM)

Figure 3. The complete CONSERN architecture

Configuration ActionSolution

Inference Engine

SymptomIdentification

SelectConfiguration Action

Inference Engine

MonitoringData Execution

Figure 4. COP/AUC decision-making engine

order to deduce contextual situations. Then, based on the knowledge that has been defined by thenetwork operator (in the form of rules and information models) or built through cognitive tasks, themost appropriate reaction is inferred. Finally, the reconfiguration parameters are calculated and thechosen reaction is enforced.

Monitoring. The monitoring function provides information to the decision-making function related tothe state of the node and/or the environment. It is responsible for the monitoring of the node’s dynamicstate (i.e. dynamic profile), which contains all the runtime information related to the status of the node.The monitoring function is responsible for context acquisition and processing in order to achievesituation awareness and enable SCM to perform appropriate reconfiguration of the network. Also,the monitoring function communicates through communication services with the IB in order to storecollected data for further processing and evaluation. Monitoring is connected to decision making aswell in order to provide notifications about certain events. For example, decision making can configuremonitoring to set specific thresholds for different parameters and when these thresholds are exceededthe decision-making function is notified.

Execution. The execution function receives decisions from the decision-making function and enforcesthem on the node. It communicates with the translation function to execute commands on the specifichardware. It also communicates with the learning function to provide information (i.e. the result of theexecution command) that may be used for the creation of new knowledge.

Learning. The learning function enhances the system with additional capabilities. Learning is theprocess by which the system collects contextual data, policies and decision-making results generatedfrom the execution of the self-x algorithms to build knowledge which will be used to improve futuredecision making and enable the system to operate proactively. Furthermore, it combines and analysesinformation from the IB together with newly received information from the execution function andgenerates knowledge.

3.1.2. Communication servicesThe communication services function (COM) enables an SCM to communicate with functional unitsor with other SCMs, e.g. the CONSERN policy manager, sensor coordinator, configurable gateway,etc. It also provides bootstrapping and auto-discovery mechanisms. Communication services andtranslation (see below) are the minimum functions a network node must implement in order to beconsidered an SCM, even if it cannot provide the full set of SCM functionality.

3.1.3. TranslationThe translation function provides the translation between abstract configuration commands generatedby the CCE into vendor/hardware-specific configurations. The translation function provides the‘middleware’ that enables a ‘legacy’ node to become a CONSERN node. On one side, the translationfunction communicates with the execution and the communication services functions by using anabstract language, while on the other side it communicates with the physical node using hardware/software/vendor-specific languages that will be defined by manufacturers.All the aforementioned functions constitute the heart of the CONSERN architecture (i.e. the SCM).

To complete the overall CONSERN network architecture, it is necessary to identify the entities andinterfaces that allow communication with a WSN and the network administration level (Figure 3).

3.2. Interacting with a WSN

The role of WSNs is important for the realization of the self-growing concept. Different types ofWSNs can provide monitoring data that traditional network equipment usually cannot. These datacould be used as input to performance optimization and fault identification algorithms, improving theirresults. Updated performance optimization schemes could be downloaded and deployed in order toperform the self-growing phase.The sensor data aggregator (SDA) collects information from a set of sensors and makes it available

to other functional entities. The existence of this entity implies clustering of the sensor nodes. It shouldbe pointed out that the SDA is a functional unit with regard to the CONSERN architectural approach.The CONSERN sensor coordinator (CSC) is an SCM dedicated to aggregate data coming from the

SDAs and reporting to other SCMs deployed on network nodes. However, its task is not the mereaggregation and forwarding of ‘raw’ data from sensors but also the application of data pre-processingtechniques and analysis. For example, instead of forwarding actual sensor data, statistical informationdescribing the sensed environment could be forwarded. The CSC also receives configuration informationfrom other SCMs in the case that reconfiguration is needed due to specific events triggered, anddisseminates the reconfiguration actions to the sensor networks under its control.The interaction between each SCM and the network administrator for rules specification, generic

business goals and performance objectives set is provided by the following entities and interfaces.We point out that sensor networks comprise a special genre of networks that can be dynamically

accommodated in the CONSERN ecosystem, thus realizing the self-growing aspect. As such, theyare also subject to optimization and repurposing. From an architectural point of view, instances ofthe CONSERN architecture are also deployed on these nodes. However, the demonstrated functionalityis subject to limitations posed by the computing capabilities of the devices. Typical examples (includingimplementation and experimentation details) appear elsewhere [30–32]. In the context of this paper, weopted to present how a simple motion sensor can be integrated into the overall environment and itsinformation properly accommodated in the decision-making process of the on–off algorithm.

3.3. Interfaces with the operator and legacy systems

The role of governance is to orchestrate self-x mechanisms in a seamless and harmonized manner, as aresult of human (network administrator) high-level policy objectives. It identifies the type of operatorcontrol into the autonomic elements. The network administration governance interface provides thecapability of setting business goals/objectives (e.g. energy efficiency in network operation) and servicerequests (e.g. accommodation of new traffic with specific quality of service characteristics in aconcrete geographical area) that have to be properly enforced in the different network nodes.Realization of these high-level policy objectives to proper selection of network nodes and enforcement

of relative actions introduces a semantic gap. This gap may be filled with the introduction of a multi-tiertranslation mechanism, known as policy continuum. Policy continuum provides a level of abstraction tonetwork administrator, as the latter is flooded with a huge amount of network node specifications.Transition among layers triggers semantic and syntactic procedures, which use mapping translators tointerface between respective layers. The policy continuum approach, which has been extensivelydescribed [33–35], can be easily adopted in our architecture, so as to achieve the proper enforcementof network administrator objectives.The CONSERN policy manager provides the interface with the network administrator (human-to-network

interface). It also holds the high-level policies that should be applied in the network. Policies aredefined by the network operator/administrator both in the initial set-up of the network and duringruntime. It communicates with the IB through the communication services lying in the SCM in orderto provide the specific policies related with that node. Also, it is connected to the CONSERNconfigurable gateway as it is very likely that a network operator may want to access the CPM remotelyvia ‘legacy’ networks. It is also connected with the CSC so as to enable the operator to apply policiesdirectly to the sensor networks (or parts of them).The policy provider is a functional unit that isintroduced to enable the operator or administrator to define high-level policies that will specifynetwork operation. The policy provider may or may not be part of the CONSERN policy manager.

The CONSERN configurable gateway is an SCM used to provide an ‘interface’ betweenCONSERN-enabled networks and legacy networks that do not support the functionality specified byCONSERN. It collects all the available contextual data from the legacy systems and reports it to theappropriate CONSERN nodes so as to take the most appropriate decisions. Such a gateway can, forexample, provide means to tunnel CONSERN-specific protocols via non-CONSERN networks. Also,it provides the means to discover the presence of a CONSERN network/node via the Internet. Thegateway is a centralized point used to initiate requests to trigger information from CONSERN entities.

4. WLAN TOPOLOGY OPTIMIZATION IN A SELF-ORGANIZED NETWORK EXPLOITINGTHE SELF-GROWING CAPACITY

Capitalizing on the CONSERN architecture we present a scheme for the WLAN topology optimization,which is optimized through the dynamic deactivation or reactivation of IEEE 802.11 APs, according tonetwork density and traffic conditions. Although the example considers IEEE 802.11 APs as itsinfrastructure nodes, the ideas can be extended to cover wireless networks in general. The proposedalgorithm for AP dynamic deactivation and reactivation has been introduced in Kousaridas et al. [36]and in this paper it is extended to exploit the self-growing concept. The latter enables the dynamicimprovement of the topology optimization scheme by allowing the utilization of the capabilities thata motion sensor provides.WLAN APs are deactivated or reactivated in order to optimize the network in terms of coverage and

capacity, while avoiding wastage of radio and energy resources. In specific geographical areas, thepotentially available wireless resources (i.e. APs) might be underutilized for a long period of timejuxtaposed with the capacity requirements (e.g. throughput, number of users). Thus, in terms of radioand energy resources, the deactivation of a set of APs could be beneficial for network areas that havemore APs than actually needed, with possible reactivation when the network conditions necessitatemore capacity. The algorithm for the topology optimization consists of the following distinct statesof operation (Figure 5):

• Monitoring state: The algorithm monitors a set of performance metrics and indicators. If a certainmetric (or combination of metrics) exceeds a given threshold it moves to a decision state, wheresome action is decided and enforced. Depending on the derived local policy, the switch-on-cellstate (within the decision state) is entered when the available resources in the network are deemedinadequate. Similarly, the switch-off-cell state (within the decision state) is entered when it isdeemed possible to lower the network’s energy consumption, while still retaining the performancemetrics within their respective limits. As soon as the action has been implemented, the systemreturns to the monitoring state. The thresholds defining when to move from the monitoringstate to the decision state depend (together with the applied algorithms) on the used policy.From the architectural point of CONSERN the discovery state is handled by the SCM’smonitoring function.

Monitoring state

Monitor PIs, criteria

Decision state

Decision on cell switch on/off

Discovery state

Node discovery

Adaptation state

Set/Calculate PIs, criteria threshold

Figure 5. Optimization framework overview

• Decision state: The network tries to maximize coverage and capacity and eventually save energyby deactivating APs. The optimization iteratively searches for a minimum number of activatedAPs that meet network performance objectives. In other words, when triggered, the decision statehas to decide whether to deactivate or reactivate specific APs. Generally, for the reactivation ofAPs, the decision state collects statistics, estimates network welfare and when to offload heavilyloaded neighbouring APs. For the deactivation of APs the algorithm may choose among the APsthat highly overlap and user handover can be facilitated. The decision state is handled by theSCM’s cooperation function within the decision function.

• Discovery state: The discovery state manages functions aiming to continuously keep track of thenetwork topology map of the APs and other neighbouring networked nodes. Initially, a contextmap of APs and its neighbouring nodes is built, storing information about supported standards,monitoring or execution capabilities and performance targets. The map is ideally updated in atimely fashion along with network topology changes. This state provides the input for theadaptation state and can be entered on a scheduled basis or when certain criteria are met, e.g. whenthe network load is too high. The discovery state is handled by the SCM’s monitoring function.

• Adaptation state: This implements the self-growing operation mode and evolves the networkoperation in terms of purpose and operational status. If the network administrator policies declarethat the purpose of the network should be changed depending on some predefined criteria (e.g. ata given time of the day) the adaptation state is (re)activated and new parameters or mechanismsare derived for usage by the monitoring and decision states. For example, a network may turn itsoperation from coverage-critical to energy-critical to provide better coverage in a single step orsmoothly in a multiplicity of steps, by adjusting optimization schemes and parameters, such aslocal coverage overlapping factor or capacity usage. This could be achieved by exploiting theinformation that a neighbouring network motion sensor provides. The adaptation state usesinformation from the policy manager together with rules learned from previous actions and ishandled by the SCM’s self-growing function.

4.1. Topology optimization scheme

In this section we present the algorithmic model for wireless network coverage optimization andspecifically for the dynamic deactivation or reactivation of a group of APs in a network area, accordingto the existing capacity requirements. The optimization is mainly performed by the decision and discoverystates, which attempt to provide quantifiable answers to the following questions:

• When to optimize?• Which AP to select?

The calculation as well as the update of the thresholds being used for the optimization take place inthe context of the adaptation state.Coverage and capacity management includes two different but interrelated tasks—especially for

wireless LAN deployment—which are inherently more complicated than other network deploymentbecause of the use of RF links, and the unplanned and in many cases random placement of APs. Eachbuilding has different RF characteristics, and the dynamic nature of the wireless channel makes thecommunication environment more volatile. The goal of the coverage management is to providenetwork connectivity at all desired locations, while capacity management undertakes to providesufficient bandwidth to satisfy clients’ communication needs. The capacity usage ratio (CUR) of a networkarea with n APs is defined as the fraction of the available capacity that is actually being used:

CUR ¼∑n

i¼1Ci

i¼1Cmaxi

where Ci and Cmaxi are the used (uplink and downlink) capacity and the maximum available (uplink and

downlink) capacity, respectively, of AP i.

The number of APs that are deployed and mainly the overlap of their transmission range should betaken into account. The network area for an AP includes the (one-hop) neighbouring APs and those(two-hop) APs that are not within each other’s reception range but are within the reception range ofassociated clients (i.e. meet the hidden terminal situation). For the calculation of the overlapping factor(OF) of a network area of the AP we use the clustering coefficient, based on graph theory [37]. Weassume that G= (V, E) is a connected, undirected graph of APs, with a set of nodes (vertices) V anda set of edges E. Let n: = |V|, e: = |E|. Parameter e corresponds to the number of existing connections(i.e. overlaps) among the APs of the network area, while n is the number of APs that constitute anetwork area. The OF is provided as follows:

OF ¼ 2en n-1ð Þ (2)

The correlation of the CUR with the OF of the APs in a network area allows for more effectiveinterpretation of the information that CUR provides, by taking into account the overlap level of theoffered bandwidth. For this reason we use the composite metric of coverage optimization opportunity(COOP), which is given by

COOP ¼ CUROF (3)

The COOP metric is useful for the identification of optimization opportunities for low load situations,where less capacity needed, as well for high load situations, where more capacity is required. A lowCOOP value means that too much capacity is provided in a very dense area, while a too high COOP valueindicates an overloaded network area, where more resources are needed. Figure 6 shows the calculatedCOOP value of three clusters, each with 15 APs, but with different levels of density (OF values) andCUR values.The high-level scheme for the coverage and capacity optimization is analysed below and is

illustrated in Figure 7. The upper part of this figure describes the role of each AP where the SCM isdeployed and the bottom part the role of the domain-level SCM for AP dynamic on/off. Thedomain-level SCM undertakes the coordination of the underlying AP SCMs that constitute thenetwork area.Each AP’s SCM periodically discovers its local topology and monitors its performance metrics. It

also periodically scans the wireless medium in order to discover the neighbouring APs (physicaltopology), thus building/updating its adjacency matrix. The MAC addresses and the channels usedby each neighbouring AP are sensed and maintained. Furthermore, each AP requests the associateduser equipment (UE) to provide the MAC addresses list of the sensed APs. The AP’s SCM collectsthe above data, builds its local physical topology graph (LTG) and transmits it to the domain-levelSCM. Specifically, each AP provides the domain-level SCM information about its operational status(monitoring state of Figure 5):

• number of the associated UEs;• AP used capacity (downlink/uplink);• AP available capacity.

(a) (b) (c)

COOP=0.57 COOP=0.35 COOP=0.77

Figure 6. COOP values for a topology of n= 15 APs: (a) OF = 0.24, CUR= 0.1; (b) OF= 0.45,CUR= 0.1; (c) OF = 0.51, CUR= 0.6

Sense Local Topology andPerformance Metrics

(Periodical Monitoring)

Build Local TopologyGraph (LTG)

Transmit the LTG to theDomain Level Concern

Entity

Update the Domain LevelTopology Graph

> DeActivationUpperBound

Build the list ofCandidate APs for

de-Activation

< DeActivationUpperBound

Activation

> reActivationLowerBound

Select AP and neighboring APsto serve the associated UEs

(Decision Making)

AP Activation/ de-Activation

(Action Execution)

< reActivationLowerBound

Select AP(Decision Making)

l– S

Identify OptimizationOpportunities

Figure 7. Dynamic AP switch on/off flow chart

Hence the domain-level SCM based on the collected information updates its awareness by buildingthe domain-level topology graph, which includes the following data:

• AAPi : set of APs that are sensed by APi;• AUEj : set of APs that are sensed by UEj;• AUEj : the AP with which UEj is associated;• UAPi : set of UEs that are associated with APi;• UAPi : set of UEs sensed by UEj;• UUEj : set of UEs sensed by UEj that are associated with the same AP (AUEj ).

Afterwards, the domain-level SCM measures the existing load levels in the network area thatconsists of n APs and calculates the OF and CUR metrics, using equations (1) and (2), respectively, andthen the network area COOP value using equation (3). Based on the calculated COOP value the domain-level SCM identifies optimization opportunities (i.e. AP deactivation) in the corresponding network area.An optimization opportunity for a low-load situation indicates that there is the possibility to deactivate

one or more APs; the goal is to avoid wasting resources in the network area, without concurrently reducingthe appropriate geographical coverage of the APs. Similarly, in a high-load situation the domain-level SCMestimates the necessity to reactivate an AP in order to address the increased capacity requirements. The pro-cess for AP switch-off is triggered if (in the context of the monitoring state of the optimization framework)

COOP < dB (4)

where dB denotes the upper bound for APs deactivation. The process for AP switch-on is triggered if

COOP > rB (5)

where rB denotes the lower bound for AP reactivation. The thresholds dB and rB are set by the networkadministrator, while the adaptation state (Figure 5) could periodically adapt the values of the thresholds.A high OF value is useful in order to address a low-loaded situation (low CUR), since there are

more opportunities for the UEs to be handed over, without reducing the access capabilities at thegeographical area that the APs cover. In the case of a high-load status (high CUR), a low- or amedium-dense network area provides more opportunities for the reactivation of an AP (previouslydeactivated). The reactivation of an AP in a high-dense network area of APs (if the capacity requirementsdo not require that) will increase further the overlapping of the selected channels and thus affect the noiseand bit error rate levels in the network area.In the following subsections we describe the scheme that the domain-level SCM uses for the selection

of the appropriate AP to switch on or off and the corresponding reallocation of UE.

4.1.1. AP switch-offIf the COOP of the network area has reached the level that satisfies equation (4), the AP switch-offaction is triggered (Table 1). First, the domain-level SCM builds the list of candidate APs for deactivation.This list includes those APs for which all their associated UEs (UEj∈UAPi ) have the capability to handover to a neighbouring AP (AP∈AUEj ), satisfying their Ci,j requirements. Then, the domain-level SCMcalculates the local COOP value of each candidate APk, as follows:

COOPAPk ¼Ck

� �OFk

where OFk is the overlapping factor of the sub-graph that is formed by APk and its one-hop-away APs;equation (2) is used for the OFk calculation. The domain-level SCM selects for deactivation the accesspoint AP�k for which COOPAPk is minimal.After the selection of the appropriate AP for deactivation, the domain-level SCM proceeds to the

reallocation of the UEs that are associated with AP�k (UE∈UAP�k ). The domain-level SCM, firstly,prioritizes the UEs of APk for the handover process, and then selects the AP where each UE shouldbe handed over. The domain-level SCM starts with those UEs that are closer to APk in order to avoid

Table 1. Algorithm for AP deactivation

Domain-level SCM: dynamic AP deactivation and load-balancing scheme

1 G← List of candidate APs for deactivation;2 For each APk ∈G3 Calculate COOPAPk ;4 End5 AP�k← Select APk with minimum COOPAPk to deactivate;6 For each UEu∈UAP�k7 Calculate βUEu

;8 End9 Do10 Select UE with maximum βUEu

;11 For each APi∈AUEu

12 Calculate γi;13 End14 Select AP with minimum γi where UEu should be associated;15 Remove UEu from UAP�k16 while UAP�k≠∅

increase of ECTxUEj

after reallocation, subsidizing also those UEs that have a small number of

neighbouring APs. The specific parameter denotes the energy consumption of UEs for thetransmission phase neighbouring APs. To facilitate this procedure, we introduce the parameter βUEj

βUEj¼ w1 1� dk;j

� �þ 1� w1ð Þ 1� v

� �(6)

where dk,j is the distance between UEj and APk , dmaxk is the transmission range of APk, ν denotes the

number of APs that UEj senses (i.e. AUEj) and n is the number of APs that constitute the network area.The weight w1, 0≤w1≤ 1, is set by the system administrator in accordance with the importance ofeach of the two terms in equation (6).The domain-level SCM selects the UE that has the maximum βUEj value and searches for APi∈AUEj,

where UEj should be handed over. UEj selects the APi (APi∈AUEj ) that has the minimum γAPlvalue:

γAPi ¼ w2 1� dk;idmaxi

� �þ 1� w2ð Þ 1� CUR

� �(7)

Through equation (7) it is assured that each UE is allocated to the nearest AP, without overloading aneighbouring AP, taking into account the CUR of the network area. The weight w2, 0≤w2≤ 1, is setby the system administrator to reflect the importance of each of the two terms in equation (7).

4.1.2. AP switch-onIn the case that the network-level COOP value satisfies inequality (5), then the process for APreactivation is initiated. The domain-level SCM first checks for deactivated APs that could be enabledin order to serve the increased capacity requirements. If more than one AP are available forreactivation, then the domain-level SCM selects AP′r , which has the maximum local COOP ratio,which is calculated as follows:

COOPAPr ¼ ∑Z

� �OFr

where z denotes the number of one-hop-away neighbours of APr, OFr is the overlapping factor of thesub-graph that candidate APr and its z neighbouring APs form. The goal is to find a high load area witha small overlapping factor.After the reactivation of AP′r, the domain-level SCM builds the list of UEs that can sense the newly

activated AP. The scheme selects to handover the UEs that are associated with an APi that has CURi

higher than the CUR of the network area and starts with the UE that is closer to AP′r. The process stopswhen the CUR of AP′r exceeds the CUR of the network area.The operation of the proposed algorithm for the dynamic activation/deactivation of an AP, and in

general the operation of a self-organized system, is controlled by thresholds, policy rules and parametersthat have been initially set by the network administrator. These thresholds in conjunction with themonitored parameters are checked in order to trigger the activation or deactivation of a network element.The administrator of the cognitive managers set the initial values of these parameters or thresholds, basedon their accumulated experience. Even if their initial values are correctly set, the evolution of networkstatus might call for their update in order to enhance decision making.A short-term forecasting method (e.g. time series-based mechanisms) could be used in order to avoid

checking a configuration action (e.g. AP deactivation/reactivation) that is triggered by a performance metricwhich has continuous variations due to temporary changes of the network area. This phase helps a SONto act proactively, avoiding the trigger of adaptations that appear to be of high uncertainty and protectsthe network from proceeding to needless adaptations that might affect its performance and stability.Specifically, the domain-level SCM decides the trigger of the deactivation/reactivation action in the

case that the value of COOP continues to exceed the thresholds that equation (4) or (5) defines after eperiodic cycles of operation. We estimate the value of COOP after e cycles, using a time series methodthat is based on the double exponential smoothing method (Holt smoothing model). A smoothing

method is designed to capture a trend, which estimates a smoothing equation when data have noseasonality or cyclicality. Having calculated COOP for the decision-making time t (COOPt), theCOOP value for the next period is predicted using the following equation:

COOPtþ1 ¼ aCOOPt þ 1� að Þ COOPt�1 þ Tt�1ð Þ (9)

where the coefficient α is the smoothing constant (0< α< 1), the value of which is important for theforecast. The selection of the best value of α is based on the minimal sum of error squared. COOPt+1 isthe forecast of the COOP value for the next period; COOPt is the calculated value of the COOP at timet; COOPt�1 is the forecast for the COOP value of the previous period; and Tt�1 is the trend estimate forthe period t� 1.Before beginning a forecast we should set the initial values of COOP0 = ‘first value of COOP’ and

T0 = 0.The trend estimate for the next period is calculated by using b (as a second parameter):

Tt ¼ b COOPt � COOPt�1ð Þ þ 1� bð ÞTt�1 (10)

Finally, the trend-adjusted forecast is denoted by

COOPtþk ¼ COOPtþ1 þ kTt (11)

where k indicates the periods that the forecasting is calculated, assuming that the same trend will bevalid for the future.Hence, using equation (11), if COOP continues to exceed the specified threshold in the time period

t+ k then the SCM is confident for addressing COOP directly and enforcing the associated configurationaction (e.g. AP deactivation/reactivation).In general, the cognitive manager has the capability for online tuning of network thresholds/parameters,

exploiting the feedback from previous actions and historic data. A history-based scheme that takesadvantage of previous events contributes towards the elimination of the ping-pong effect and therespective management overhead in terms of computational and communicational resources. Further-more, the learning capabilities of the SCM process the accumulated knowledge from all appliedaction update thresholds or parameters that are used by the decision-making algorithms. Henceping-pong effects are minimized in future adaptations, leading thus to a smoother operation of theself-managed network.

4.2. Evolution of the topology optimization scheme using motion sensor capabilities

The presence of a WSN can enhance the capabilities of the mechanism by providing spatial/geographicalinformation. The activation of a WSN leads to the update of the self-optimization scheme for thedecision-making phase, as follows:

• improves the selection of the AP to deactivate in order to reduce the possibility of coverage holes,especially for indoor scenarios, e.g., office

• reactivates an AP when a presence is detected by the WSN in a previously empty room.

In the sequence diagram (Figure 8) a new wireless sensor is activated and the self-growing processis depicted. A motion detection sensor is wirelessly attached to the sensor data aggregator (SDA) andthe sensor registration process is initiated (steps 1–3). The SCM is informed via the CSC about thepresence of the new sensor and, based on the information that the sensor collects, decides whether anew functionality could be developed for the COP or AUC blocks (steps 4–7). The SGN retrievesdecision-making and knowledge schemes that AUC and COP use and assesses whether a new, moreadvanced scheme could arise (steps 8–10). In the case that SGN has built a new functionality theprocess of the respective entity (COP or AUC) is paused in order to upgrade COP or AUC functionality(steps 11–20). After the self-growing phase the SCM informs SDA and CSC in order to register the newwireless sensor (steps 21–24).Activation of the wireless sensor increases the quality of the information that is available for the

selection of the most appropriate AP for switch-off, by improving and making more accurate the

Figure 8. Sequence diagram for the self-growing process using the CONSERN architecture entities

optimization phase. The WSN provides spatial information about APs and mobile terminal relativepositions, which are not available through mobile terminals’ received signal strength (RSS). The updatedversion of the coverage optimization scheme is depicted in Figure 9. After the building of the list ofcandidate APs for deactivation and before the calculation of the local COOP of each candidate AP, onemore check takes place. Specifically, the domain-level SCM retrieving data from the wireless sensors thatare placed near the corresponding AP checks whether there is a motion (i.e. human presence) in the areathat the sensor monitors. In the case that the sensor detects motion in an area (e.g. office) that is notcovered by other APs or is considered as a low coverage place (e.g. ‘blind area’), then the correspondingAPm, regardless of its COOP value, is not considered as a candidate AP for deactivation, and consequentlyit is excluded from the candidate APs list.Furthermore, the sensor data for the detected motion level of an area could also be helpful for the

reactivation scenario. If an AP of a specific office is deactivated and a human (i.e. potential end user)appears, being detected by the corresponding sensor, then the AP of this office is directly activated inorder to potentially serve the end user.

5. PERFORMANCE RESULTS

In the context of this section, the actual implementation of the previously described architecturefollowed by the realization and real-life deployment of a self-growing test case are presented.The experimental assessment effort we present comprises an in-house implementation and validation

effort. As such, numerous limitations were posed, by various factors. Indeed, the concept is extendableand can accommodate numerous technologies, thus adding complexity to the effort. Despite that, theexperiments comprise a real-life validation effort, which was conducted in a real office environment.

Sense Local Topology andPerformance Metrics

(Periodical Monitoring)

Build Local Topology Graph(LTG)

Transmit the LTG andPerformance Metrics to the

Domain Level Concern Entity

Update the Domain LevelTopology Graph

> DeActivationUpperBound

Build the list ofCandidate APs for de-

Activation

< DeActivationUpperBound

Activation

> reActivationLowerBound

Select AP and neighboringAPs to serve theassociated UEs

(Decision Making)

AP Activation/de-Activation

(Action Execution)

< reActivationLowerBound

Select AP(DecisionMaking)

CM Identify Optimization

Opportunities

Filter the list ofcandidate APs based

on the WSN Data

WirelessSensor

Prompt APRe-

activation

PeriodicTransmission of

WSN Motion Data

Figure 9. Dynamic AP switch on/off scheme: after WSN integration and self-growing process

A detailed API accompanied by implementation details has been purposely omitted in order to keep thedocument small and concise. However, a design note of the developed system (extending beyond thescope of this publication, however) accompanied by deployment libraries is available [38].

5.1. Experimental facilities description

First we provide details of the implementation effort for the deployment of the self-growingarchitecture described previously. This paragraph builds on the theoretical outcomes of the previoussections and establishes the validity and viability of the proposed solution. Initially, an overview ofthe hardware and software platform is provided and the mapping of the architecture on the testbed isdescribed.Note that the presented experimentation case is extendable and can accommodate numerous

technologies (thus justifying the concept of heterogeneity); however, these technologies were notavailable at the time of this writing. Therefore, we opted for a WiFi-only implementation and justifiedthe extension of the architecture to heterogeneous environments through the discussion on thearchitecture in Section 2.

5.1.1. Hardware infrastructureThe experimentation facilities consist of a variety of equipment including hardware and softwarecomponents. Figure 10 depicts the available network elements. Several interconnected routers andswitches provide access to the Internet. The core network supports different access technologies(both wireless and wireline). A number of IEEE 802.11 APs are also available, providing connectivityto a set of IEEE 802.11-enabled mobile terminals. Additionally, there is connectivity to a wirelesssensor network via a sensor coordinator. Finally, a number of servers provide applications andservices to end-users. Overall, the testbed has the ability to monitor energy consumption in the wirelessaccess points.The core network consists of Linux-based routers and several multiport switches. The Wi-Fi access

points are Soekris devices net5501 [39] (500MHz AMD Geode LX, 128–512MB DDR-SDRAM,eight 10/100Mb Ethernet ports and ATHEROS MINIPCI 802.11A/B/G) running Linux (kernelversion 2.6.33). These devices are fully programmable, thus enabling effective and efficient prototyping.Phidget motion sensors are interconnected with the access points and are handled by a coordinatingentity [40].The ‘terminals’ side hosts four MSI Wind U100-483JP netbooks equipped with IEEE 802.11

interfaces. Furthermore, three mobile handsets (HTC Desire [41]), Nokia 770 [42] and MotorolaA910 [43] are also deployed, supporting access to Wi-Fi networks as well as to 3G infrastructures.

Figure 10. Testbed used in the context of dynamic AP switch on/off prototyping effort

5.1.2. Software infrastructureThe OSGi framework has been used for the implementation of the various architectural concepts andtechniques. The OSGi software engineering framework provides us the capability to inherently have amodularized solution, activating and deactivating the appropriate software bundles, especially in thecontext of the self-growing phase, where new schemes or operation should be enabled.Software bundles are developed in Java [44] and are used for managing the network devices and

monitoring existing resources, e.g. energy, signal strength, radio frequencies. Software bundles arealso deployed on the terminals in order to access specific characteristics of the devices (energyconsumption, wireless channels, CPU levels, memory usage and battery levels).

5.1.3. Mapping on the architectural frameworkIn this section we describe the mapping of the proposed architectural framework (Section 2) to a realsystem in order to present in more detail the operation of each functional block as well as theirinteractions.The SCM of each AP is instantiated as a set of software bundles each with a specific set of functionalities

(Figure 11).

• Information base: The IB is employed in order to store information related to the status of thedevice (e.g. thresholds for AP reactivation, deactivation, policy rules for the activation of self-growing phase, data of motion sensors for human presence). Its format is common across allnetwork devices (e.g. terminal, access point). It is used by all subsequent bundles in order tostore, retrieve and evaluate information. Additionally, decisions taken by the decision-makingbundle are stored in the IB.

• Communication services: This bundle implements the communication between all involvednetwork entities. Transmitted information is formatted according to the information modelprovided by the IB.

• Monitoring: The monitoring bundle undertakes the task of monitoring the operational environmentof the device. It constantly retrieves information from all network adapters and stores it in the IB. Anindicative list of the parameters monitored (per interface) is the following: packet error rate, bytessent, bytes received, Tx-power, noise, link quality, discarded packets, etc.

• Decision making: The decision-making bundle undertakes the tasks of collaborative decisionmaking, accommodation of novel network elements, reconfiguration and adaptation accordingto network conditions. In the context of the described prototype it triggers the processes of channelselection, access point switch on/off and the accommodation of new devices (i.e. sensor). Simplystated, the decision making evaluates the information received from monitoring, employs thefunctionality of the communication services in order to exchange information with otherdecision-making entities and stores decisions in the IB. Decisions are also forwarded to theexecution in order to be implemented on the underlying hardware infrastructure.

• Execution: The main task of the execution bundle is the actual execution of high-level instructions/directives on the underlying hardware. In the context of the described prototyping activity it

WiFi AP

ogy Decision Making

CooperationAutonomic

ControlSelf-Growing

Figure 11. The SCM component as implemented on the AP device

executes the directives of decision making (i.e. RF on/off, channel deployment, handover ofassociated terminals, accommodation of motion sensor, loading of new bundles).

• Self-growing: The self-growing bundle checks the local topology in order to identify the self-growing capabilities. In the case that a motion sensor is activated in the AP range and data ofmotion sensors could be received, the self-growing bundle triggers the process for the evolutionof the decision-making bundle. An updated version of the AP switch on/off scheme isdownloaded and installed. Furthermore, the IB is updated in order to support the data and therules for the updated scheme.

The first enabled AP is assumed to undertake the role of the cluster head (i.e. domain-level SCM),synchronizing the interactions of all other APs and facilitating the collaborative decision-makingprocess, by providing a communication reference point. Thus it implements functionalities related tothe operational and administrative layers of the functional architecture.The SCM implementation on the mobile devices appears in Figure 12. Essentially, the implementa-

tion addresses simple issues such as monitoring of the operational environment (monitoring module),implementation of the handover directives (execution module), communication of data and controlinformation to the AP on which it is attached (communication service module) and storage of informa-tion based on a common information model (IB module).Finally, the sensor coordinator (Figure 13) uses an information model in order to store monitored

(implemented in the context of the monitoring module) information. The information model as wellas the storage capability are available through the IB module. Furthermore, the communication servicemodule contains all the classes that enable the sensor to communicate with the data aggregator.Finally, the autonomic control module (the only functionality of decision making used in the sensor)in conjunction with the execution implement any changes/adjustments directed by the directing AP,which essentially plays the role of a sensor coordinator.

Figure 12. The CCE component as implemented on the client device

Figure 13. SCM deployment as implemented on the sensor coordinator device

5.2. Experimentation test case

Towards assessing the validity and viability of these concepts and techniques we designed andimplemented two realistic experiments related to coverage and capacity under energy consumptionoptimization constraints. The first was conducted with limited calibration and enabled us to draw a firstset of conclusions, while the next one was performed under controlled laboratory conditions andhelped us evaluate special situations which could not be assessed in the first case. For the secondexperiment we also tested the concept of self-growing by incorporating in the decision mechanismspatial awareness information from security-related sensors. In the following we present the detailsof these efforts.

5.2.1. Experiment case 1: a day in the officeThis experiment took place in our office facilities,1 where 15 researchers work on a daily basis. First,four Soekris APs were deployed in our offices following the topology presented in Figure 14. Thedevices implemented the CONSERN architecture incorporating the APs on/off algorithm. All APswere equipped with two antennae; one was deployed as an AP, using hostapd daemon [45], whilethe second was used to scan the wireless environment. Each AP deployed its own network and routedthe information to the Internet through NAT. APs were connected through the backbone network andwere controlled by a standalone machine which aggregated information. The network layout used inthe experiments is depicted in Figure 15.The laboratory members (15) used these access points for 12 consecutive hours (from 11:00 CET

(Central European Time) until 23:00 CET on 28 May 2012) in order to access the Internet and performall normal, working-day, activities. Overall traffic throughout the day ranged from 1 to 10 Mbps, whileAPs were configured to serve clients at 5.5 Mbps.The results obtained by the experiment appear in Figure 16. The experiment was initiated at 11:00

CET. In order to trigger the on/off algorithm we deliberately set extremely low values for on and offtriggers: 0.4 and 0.1 respectively. Due to the limited area for experimentation, OF (overlapping factor)was always 1, signifying that all devices could scan each other. Thus an AP would be directed toswitch off if global traffic fell below 10% of the maximum available capacity (i.e. assuming that allfour APs were active, the lower threshold was 2.2 Mbps). Similarly, an AP would be directed to switchon if global traffic exceeded 40% of the maximum available capacity (assuming three active and onedeactivated AP, the latter would be switched on if global traffic surpassed 6.6 Mbps).2

Study of the graph reveals the added value of the algorithm. From 11:00 to 12:00 CET, two attemptswere made to switch on an additional device; however, that could not be fulfilled. During the lunchbreak, from 12:00 to 13:00, network requirements fell below 2.2 Mbps; thus an AP was deactivated.As soon as employees were back in the office and traffic surpassed the activation bound, the previouslydeactivated AP was activated once again. The network remained stable until people started leaving.Progressively until 18:00 CET all devices except from the cluster head were switched off. The clusterhead was the only device that remained active until the end of the experiment.The deactivation of an AP’s RF reduces its energy consumption from 9.73 to 7.79W, thus inducing

a 20% reduction per access point. Given the fact that 1W= 1 J/s, the aggregated gain because of the 3RF deactivated from 18:00 until 23:00 can be calculated as follows:

• four fully functional APs from 18:00 to 23:00 require 700.56 kJ;• one fully functional AP and three with their RFs suspended require 595.80 kJ.

Thus a network operating with CONSERN-enabled APs implementing the on/off algorithmsrequires 15% less energy than the same network operating with non-CONSERN devices.In the context of this paper we provide an aspect of the experiment. The overall effort validated

numerous algorithms developed in the context of the project. The interested reader can watch thewhole story [46].

1Self-evolving Cognitive and Autonomic Networking Laboratory: http://scan.di.uoa.gr2Recall that COOP=CUR°F; thus if OF= 1, COOP=CUR.

Figure 14. Physical topology: office layout and AP placement

Figure 15. Network topology

5.2.2. Experiment case 2: topology optimisationIn the next experiment, we focused on creating a non-overlapping graph (e.g. OF< 1); thus wedeliberately lowered the transmission power of the devices. The latter is necessary due to the fact thatthe experiment takes place in a laboratory environment; thus space is limited while in parallel we needto create specific sensing conditions, as described later. In parallel, during the course of the experiment,a motion detection sensor was attached to one of the APs in order to showcase the realization of theself-growing paradigm. Finally, in order to create extreme traffic conditions on demand (unattainable withnormal usage) we used the distributed Internet traffic generator (D-ITG) [47], which is a Java-basedsoftware tool that generates traffic at NKUA endmachines. The experimentation environment consisted thistime of six Soekris APs and various laptops acting as mobile clients. The main configuration parameters arepresented in Table 2.Figure 17(a) depicts the allocation of the APs in the office environment. The total nominal capacity

of the described topology that consists of six APs is 324 Mbps (6 × 54 Mbps). Given the transmissionpower adjustment, each can sense only a subset of its actual neighbourhood. Essentially, two APs areconsidered neighbouring if they are within each other’s transmission range. Thus the proximity graphis presented in Figure 17(b). We assume that G= (V, E) is a connected and undirected graph of networknodes. N= {N1, N2, N3, …, Nn}, n ∈ℵ is the set of the network elements (i.e. APs) of the graph

Figure 16. Evolution of COOP during the experiment on the lab premises.

Table 2. Soekris AP testbed configuration

Testbed parameter Value

AP SoekrisPHY IEEE 802.11b/gFrequency 2.4GHzBandwidth 3.5MHzCapacity 54 MbpsrB (reactivation threshold) 0.35dB (deactivation threshold) 0.75

network area. The initial OF is 0.56. Figure 17(b) depicts the calculated COOPt value for each timeinstance t of the demo scenario, as the latter is calculated by the corresponding OFt and CURt values.According to the scheme presented in Section 3, all APs monitor status metrics (e.g. capacity usage,

overlapping factor and associated terminals’ received signal strength) that are periodically transmittedto the SCM domain-level entity. Based on the collected data the latter calculates the COOP metric anddecides whether the switch-on or switch-off of an AP is necessary in the region that the cluster defines.In the presented topology the specified thresholds for deactivation and reactivation trigger are set torB= 0.75 and dB= 0.35. Taking into account the previous graph as well as the CUR of each AP(Table 3) the overall (domain-wide) CUR and COOP values are 0.19 and 0.41, respectively. Note thatthis COOP value does not trigger any optimization action, since the available resources are not under/over-utilized. The CUR is the result of the traffic that the terminals receive from the traffic generator.During the experimentation we deliberately reduce the overall traffic via proper manipulation of the

traffic generator. Thus bandwidth requirements are reduced and less capacity is required. Table 4presents the updated CUR values for each AP. Consequently, the global (domain-wide) CUR and COOPvalues are now 0.13 and 0.34, respectively. The latter, in conjunction with the traffic and density featuresof the APs, triggers the deactivation phase based on Figure 18. The preferable AP for switch-off is se-lected by the SCM after considering the number of associated terminal(s) as well as their options forreassignment to neighbouring APs. The load-balancing scheme for terminal reallocation also considersthe received RSS and the neighbouring APs’ existing load level, both metrics assessed per terminal basis.

Office 1 Office 2

AP 1 –Cluster Head

Figure 17. Actual deployment of APs in an office environment

Table 3. AP initial capacity and coverage values

AP CUR Local OF Local COOP

2 0.2 0.70 0.324 0.2 0.70 0.321 0.2 1.00 0.205 0.2 0.67 0.346 0.2 1.00 0.203 0.15 1.00 0.15

Table 4. AP capacity and coverage values: before deactivation

2 0.1 0.70 0.204 0.05 0.70 0.121 0.2 1.00 0.205 0.1 0.67 0.226 0.2 1.00 0.203 0.15 1.00 0.15

For the specific case, AP4 exhibits the minimum local COOP and is therefore selected for deactivation.SCM notifies the corresponding AP, which in turn forwards the decision to the attached terminal(s). First,AP4 terminals hand off to AP2 and then AP4 is switched off (Figure 19). Evaluating Figure 18 and

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Time Instance

Re-activation Threshold COOP De-activation Threshold

Figure 18. COOP value during test case experimentation

Office 2Office 1

AP 1 – Cluster Head

Figure 19. Deployment of APs in an office environment after deactivation

Table 5, the reader will notice that the COOP value after the deactivation phase exceeds the dB threshold(COOP5= 0.48, CUR5= 0.16, OF5 =0.4), deducing that the energy and radio resources are rationally andmore efficiently used.After deactivation, the topology consists of five active APs. During the second part of the experiment

we deliberately increase the traffic levels by reconfiguring the traffic generator (halting and reinitiatingwith higher load levels) on all clients; consequently the CUR of the specific network area is increased,given the fact that the offered capacity essentially remains the same. As soon as the COOP exceeds thepreset rB threshold the optimization function is triggered, this time attempting to increase the availablecapacity (CUR11 = 0.54, OF11 = 0.4, COOP11 = 0.78).

Table 5. AP capacity and coverage values: after deactivation

2 0.15 0.50 0.394 0 0 01 0.2 1.00 0.205 0.1 0.67 0.226 0.2 1.00 0.203 0.15 1.00 0.15

Given the fact that only one AP is in stand-by mode, there is a single candidate for reactivation—AP4—which is enabled. Terminals are once again reallocated so as to ensure efficient usage of theavailable bandwidth and avoid congestion on the other APs. Tables 6 and 7 describe the local COOPof each AP before and after the reactivation phase. Reaching a state of tranquillity, all APs are enabledagain (Figure 18) and the COOP value is reduced below the rB threshold, showcasing that radio andenergy resources are efficiently used (CUR12 = 0.45, OF12 = 0.53, COOP12 = 0.65).The energy consumption of each Soekris AP device operating in an active state is, on average,

9.73W. In this measurement all the software and hardware modules that are necessary for the efficientoperation of the AP are considered since they are deployed. The deactivation of the RF component, inorder to set the operation of the Soekris AP in the stand-by mode, reduces the energy consumption tothe level of 16W. The total traffic that the APs transmit in the cluster area remains the same as beforethe deactivation, since the downlink (DL) traffic of the deactivated AP is served by neighbouring APs.Hence there is no change in the power that is consumed for the DL traffic. It should be pointed out thatthe energy savings coming from the deactivation of software modules/components that are deactivatedduring idle time is negligible. After the AP deactivation some of its software modules remain active,and wait for reactivation notification from the domain-level SCM, when there is the need for additionalcapacity in a high load case.Taking into account the above scenario, we observe that the deactivation of an AP RF reduces its

energy consumption to the level of 7.79W, observing again a 20% energy reduction.

5.2.3. WSN integration and self-growingUp to now we have showcased how topology optimization and consequently energy saving can beachieved, by exploiting the CONSERN architecture as well as the proposed scheme. The next part

Table 6. AP capacity and coverage values: before reactivation

2 0.6 0.50 0.64 0 0 01 0.4 1.00 0.45 0.9 0.67 0.96 0.3 1.00 0.33 0.5 1.00 0.5

Table 7. APs capacity and coverage values – after reactivation

2 0.3 0.70 0.34 0.3 0.70 0.31 0.4 1.00 0.45 0.9 0.67 0.96 0.3 1.00 0.33 0.5 1.00 0.5

of the experiment focuses on enhancing the decision-making capability of the SCM by incorporatingspatial awareness features (Figure 20). The inherent self-growing capability of the developed solutionfacilitates the exploitation of the monitoring data that heterogeneous networking objects, such asWSNs, provide. WSNs will have large-scale deployments and various applications, such as environmentalmonitoring and military surveillance. Their monitoring data could be also used for multiple objectives aswell as for the problem that is addressed in this paper, improving the quality of topology optimization.The motion detection sensor that is used in the demonstration provides data for building spatial awareness.As soon as a wireless sensor is attached to an AP, a new software bundle—in real systems we

assume that it will be provided by the network operator, or the manufacturer—is downloaded andinstalled (or activated if already exists) in order to utilize the information that the specific sensor provides.The latter enables the processing of the (spatial) information provided by the sensor and its exploitation inthe context of the collaborative decision-making process. This process showcases the self-growing aspectintroduced by the CONSERN architectural framework. The re-execution of the first step of this scenarioresults in the switch-off of a different AP, reducing the possibility of creating a coverage hole.In the scheme proposed above, the coverage that the available APs provide is measured using APs

and terminals’ sensing capabilities. If the RF interface of one or more terminals is not enabled then therespective APs cannot detect their presence in the specific area. Hence there is the danger of creatingcoverage holes in the case of a deactivation, switching off the ‘wrong’ AP. If the domain-level COOPis below dB threshold (CUR16 = 0.13, OF16 = 0.53, COOP16 = 0.34), and candidate APs for deactivationexhibit similar COOP values (AP2 and AP4; Table 8) then the detection of motion/presence via theWSNin the area of AP4 leads to the deactivation of AP2. According to the scheme presented in Section 3.2 thedecision-making scheme of the SCM selects AP2 for deactivation (Table 9). The presence of the WSNand its exploitation through the self-growing concept allows us to improve the coverage after the topologyoptimization having the same level of gain in energy consumption.

Office 1 Office 2

AP 1 – Cluster Head

Figure 20. Deployment of APs in an office environment (WSN)

Table 8. AP capacity and coverage values: before deactivation (WSN integrated)

2 0.05 0.70 0.124 0.05 0.70 0.121 0.2 1.00 0.205 0.1 0.67 0.226 0.2 1.00 0.203 0.15 1.00 0.15

Table 9. AP capacity and coverage values: after deactivation (WSN integrated)

2 0 0 04 0.1 0.50 0.391 0.2 1.00 0.205 0.1 0.67 0.226 0.2 1.00 0.203 0.15 1.00 0.15

6. CONCLUSIONS

In this paper we have presented a functional architecture that supports the notion of self-growing inautonomic networking environments. With self-growing we can achieve the reconfiguration ofnetworks and nodes in terms of purpose and operational status. We also allow the interoperation ofpreviously vertically deployed networks. This is a very important issue, since the information collectedfrom various entities can be used to improve the efficiency or the robustness of network managementmechanisms. The feasibility of our defined architecture was presented in a real implementation, andthe enhancement of a SON mechanism using a self-growing mechanism was also illustrated. The testcase and the performed experiments prove the validity and the viability of the proposed solutions,showing coverage improvement, energy savings and an improved decision-making process based onadditional information. Our future plans include the application of self-growing in more complexsituations, as well as the study of possible conflicts that may arise during the execution of self-growingand typical autonomic network management actions.

ACKNOWLEDGEMENTS

The research leading to these results has received funding from the European Community’s SeventhFramework Programme (FP7/2007-2013) under grant agreement CONSERN No. 257542.

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AUTHORS’ BIOGRAPHIES

Apostolos Kousaridas has a PhD from the Department of Informatics & Telecommunications at the University ofAthens. He received his B.Sc. degree in Informatics and his M.Sc. degree in Information Systems from theDepartment of Informatics at Athens University of Economics and Business. He has participated in a numberof European research projects, namely E2R, E2R II, E3, Self-NET, CONSERN, UNIVERSELF and METISEU-funded projects. He also serves as a University of Athens delegate at the ETSI Autonomic Future Internet(AFI) Industry Standardization Group (ISG). His research interests include complex self-organizing wirelessnetworks, cognitive network management, energy and radio resource management, and software engineering.He has more than 30 publications in International journals and conferences.

Alexandros Kaloxylos received the B.Sc. degree in Computer Science from the University of Crete, Greece, in1993, the M.Phil. degree in Computing and Electrical Engineering from the Heriot-Watt University, Scotland,in 1994 and the Ph.D. degree in Informatics and Telecommunications from the University of Athens in 1999.From 1990 to 1993 he was a staff member of the Computer Centre of the University of Crete, and a researcherin the Foundation of Research and Technology Hellas (FORTH). From 1994 to 1995 he was a research associateat the University of Wales. From 1995 until today he is a researcher at the Communications Network Laboratoryof the University of Athens. In 2002 he joined the faculty of the University of Peloponnese, where he is presentlyan Assistant Professor in the Department of Informatics and Telecommunications. He has participated innumerous projects realized in the context of EU programs as well as National Initiatives. He has published over100 papers in international journals and conferences. He is a senior member of IEEE and a member of the editorialboard of the IEEE Communication’s Society Surveys and Tutorials Electronic Journal.

Panagis Magdalinos is a researcher in the SCAN group, in the Department of Informatics and Telecommunicationsof the University of Athens (UoA). In 2010 he acquired his PhD diploma entitled “Linear and Non LinearDimensionality Reduction for Distributed Knowledge Discovery” from the Department of Informatics of the AthensUniversity of Economics and Business (AUEB). He also holds an M.Sc. in Information Systems from AUEB and aB.Sc. in Informatics and Telecommunications from UoA. Since 2004 he has participated in a number of Europeanresearch projects, namely E2R, E2R II, E3, SelfNET, CONSERN, SmartAgrifood, METIS and LiveCity. Hisresearch interests focus on supervised and unsupervised knowledge extraction from distributed data collections(e.g., Learning and Mining in Distributed Environments, Parallel Data Mining).

Thanos Makris is co-founder and CEO of Trebbble, a mobile strategy and development firm. He is the cloud soft-ware expert and database architect in Trebbble and has extensive experience in a plethora of server-side web tech-nologies. In the past, Athanasios has worked for various companies and institutions (including Ericsson AB,SCAN group) gaining solid knowledge and experience in the design and deployment of network architectures,real-time communication systems, and large scale IT infrastructures. Athanasios received a B.Sc. in Telecommuni-cations Science and Technology with honors from the University of Peloponnese, Tripolis, Greece, in 2006 and anM.Sc. in Wireless Systems from the Royal Institute of Technology (KTH), Stockholm, Sweden, in 2008.

Georgios Koudouridis received a B.Sc. degree in Computer Sciences from the Department of Computer and Sys-tems Sciences, KTH/Stockholm University, in 1995 where he also studied towards his MSc degree and worked asa part-time teacher till 1997. In 2013 he received his Lic. Tech. degree in telecommunications from the School ofElectrical Engineering at the Royal Institute of Technology, KTH, as an industrial Ph.D student. From 1997 till2003 he was working as a researcher at Telia Research where he led and contributed to technical projects aimingat applying software agent technology from AI to telecommunications. From 2003 till 2008 he was withTeliaSonera Mobile R&D where he continued his work on mobile communications research in the areas of mul-tiple-radio access optimization and all-IP networking. He represented Telia in numerous international fora and in-ternational collaborative R&D projects. He joined Huawei R&D Center Sweden in 2008 and since then he is asenior researcher in Radio Network Technology research group focusing on radio resource management and op-timization techniques in wireless heterogeneous networks. In his current position at Huawei he pursues his Ph.Ddegree in the area of telecommunications at the Royal Institute of Technology, School of Electrical Engineering,KTH/EE. His main research interests include cognitive radio networks, self-organized networks, adaptive spec-trum sharing, and energy-efficiency in wireless communications.

Gunnar Hedby is a senior researcher at Huawei since 2006. He holds a Licentiate degree in Data Transmissionfrom Linköping University (Sweden) and has been working in the field since 1978. He has more than 10 yearsexperience as a technical leader in the military industry. He has been active in GSM (GPRS) and LTE

standardisation and was the rapporteur of 3GPP TS 43.129 (Packet Switched Handover). His research interests arein the areas of mobile wireless networks with focus on the performance of next-generation networks and has beeninvolved in the FP7 CONSERN project where his focus was on green cellular radio research.

Nancy Alonistioti has a B.Sc. degree and a PhD degree in Informatics and Telecommunications (Dept. ofInformatics and Telecommunications, University of Athens). She has working experience as senior researcherand project manager in the Dept. of Informatics and Telecommunications at University of Athens. She hasparticipated in several national and European projects, (MOBIVAS, ANWIRE, E2R, LIAISON, E3, SELFNET,SACRA, CONSERN, UNIVERSELF etc) and has experience as Project and Technical manager of theIST-MOBIVAS, IST-ANWIRE, ICT-SELFNET, ICT-CONSERN projects, which had a focus on reconfigurablemobile systems, cognitive mobile networks and future internet. She is co-editor and author in “Software definedradio, Architectures, Systems and Functions”, published by John Wiley in May 2003. She has served as lecturer inUniversity of Piraeus and she has recently joined the faculty of Dept. Informatics and Telecommunications ofUniv. of Athens. She is TPC member in many conferences in the area of mobile communications and mobileapplications for systems and networks beyond 3G. She has over 100 publications in the area of mobilecommunications, reconfigurable, cognitive and autonomic systems and networks and Future Internet.

Integrating the self-growing concept in a self-organizing wireless network for topology optimization

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