clustering in ad hoc networks

8/13/2019 Clustering in Ad Hoc Networks

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Algorithms For Clustering In Ad Hoc Networks

Presented For Your Enjoyment By Team 4Jim KileDon Little

Samir Shah


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What Is An Ad Hoc Network?

Wireless computer networkNo central control

Computers talking to each otherSuitable for

Conference rooms

ClassroomsBattlefieldsWearable computing


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What Is Clustering In Ad-hocNetworks?

Partitioning wireless device nodesinto groups

Each group has clusterheadOversee channel allocationMessage routing within clusterMessage routing between clusters

Ordinary nodes within theclusterhead's transmission range


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What Are Benefits Of Clustering?

Controlling spatial reuse of sharedchannelBuilding/maintaining cluster-based virtualnetwork architectures


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What Are Benefits Of Clustering?Routing

Minimizing amount of data exchangedfor routing

Lower cost fewer routes

Simplify routing tables/structure Abstract network structure

Higher level structure unaffected by localtopology changes


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What Are Goals Of Clustering?

1) At least 1 neighboring clusterhead Allows fast communications between nodes

2) Nodes connected to best" clusterhead 3) Clusterheads well scattered throughout

the network


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Why Is Clustering Important?Infrastructure

WiredWell defined infrastructureNetwork structure is staticLink failure is infrequent

WirelessInfrastructure-less

Rapid topology changeFrequent link failures

Routes calculated frequently


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Why Is Clustering Important?Range

WiredTransmission range is largeEach node responsible for

Its own communicationsWireless

Transmission range is small relative to network sizeEach node responsible for:

Its own communicationsForwarding communication from others ( multihop )


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Why Is Clustering Important?Power

WiredVirtually unlimited power

WirelessVery limited power


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Why Is Clustering Important?Routing Algorithm

WiredPre-calculated routing algorithmDesigned for relatively stable networks

WirelessNew algorithmDesigned for

Mobile unitsTopology continuously changing


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How are Clusters Represented?

Graph G = (V E)Vertices (V) represent individual nodesEdge (E) connection between two verticeswithin range


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Abstracting Network Topology

BLUE = network structure

BLACK VERTICES = clusterheads

BLACK EDGES = virtual connectionsbetween clusters


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How Are Clusterheads Chosen?

Approximating Minimum Size Weakly-Connected Dominating Sets For ClusteringMobil Ad Hoc Networks

Criterion: domination in graphs

Distributed Clustering For Ad Hoc NetworksCriterion: generic weight


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FIRST PAPER

Approximating Minimum SizeWeakly-Connected Dominating

Sets For Clustering Mobil Ad HocNetworks


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Papers Main Contribution

Finding a completely distributedalgorithm for identifying small weaklyconnected dominating sets


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Algorithms Presented

Presented 5 algorithms Analyzed 2 algorithms

Their most important algorithm coveredhere Algorithm V Distributed Asynchronous Approach


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Dominating Set Of A Graph

S

V vV S

over aoadjacentorineitheris

exevery vertsuch that,subsetvertexais

E(VGgraphaof setdominatingA


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Black Vertices Form Dominating Set


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Black Vertices Form Dominating Set

Vertices of dominating set =clusterheads

Assign each vertex to clustercorresponding to dominating vertexOptimize smallest dominating set

Simplify the network structureFind ing a m in imu m s ize dom inat ing se tin a general g raph i s np -co m ple te


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Connected Dominating Set (CDS)

Dominating set whose induced subgraphis connectedInduced subgraph used for routingmessages between clustersConnectivity requirement causes largenumber of clustersFind ing m in imu m s ize co nnec teddo m inat ing se t is NP-co m ple te


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Connected Dominating Set

BLUE = network structureBLACK VERTICES = clusterheads

BLACK LINES = induced subgraph


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Weakly-Connected Dominating Set(WCDS)

set vertetheas neighborstheirof alland Sinverticestheincludes

.))(,(gthis)( byinduced eaklySubgraph w

w

w

S

xS S N E S N S

V S S


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Weakly-Connected Dominating Set(WCDS)

Remove edgesResulting in a sparser structure

Can yield fewer clusters than CDS


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Desired Graph Properties

Goal is to find a small weakly-connecteddominating set in order to abstract thenetwork structure as much as possibleSmaller values are preferredImprovement number of pieces thatwould be merged into a single cluster ifthat piece were clusterhead


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Assumptions

We assume every node knows the roleand piece ID information of all itsneighborsEach device has own internal decisionmechanism to determine its own (local)best candidateMultiple clusterheads are grown inparallel


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How Are Node Roles Shown?

Algorithms uses color to display role ofthe vertex

White not assigned to any clusterGrey assigned to a cluster but notclusterheadBlack clusterhead


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Algorithm - Beginning

Each node starts out NOTconnected to any other node

Initially white-not connected to clusterChange color as the algorithm progresses


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Algorithm - Each Iteration

Gray and white node calculate clustersize if they were the clusterheadNode with largest improvement in itsclosed neighborhood is new clusterheadChosen candidate node colored black

Neighboring white verticesColored gray - member of clusterMerged into the cluster


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Algorithm - Termination

Algorithm terminates when no pieceshows improvementBlack vertices constitute a Weakly-Connected Dominating Set


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Prior To First Iteration


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After First Iteration


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Authors Evaluation Methodology

Generate random graphs repeatedlyRan this algorithm against test algorithmfrom othersCompute dominating set sizeSmallest dominating set is best


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Authors Evaluation Setup

Place vertices randomly in a rectangular areain 2D-plane

Two levels of density40 to 200 vertices

Assign each node a transmission range According to a normal distributionCentered at a predefined expected value

When two nodes are placed within range of

each other An edge is added between the verticesSimulates a reliable link between them


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Authors Evaluation Conclusion

For each randomly generated networkMeasure the dominating set size resultingfrom the algorithms

Authors believe demonstrated that theiralgorithm generated smaller dominatingsets


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Why They Are Wrong*

No reason to believe that algorithmachieved optimum placement

Could be local optima

No reason to believe that algorithm theytested against is idealEvaluated in 2D world

Does this generalize to 3D world?

*terminology per Dr Cha


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SECOND PAPER

Distributed Clustering For Ad HocNetworks


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Algorithm Presented

Presented 2 algorithmsSelected the Distributed Clustering

Algorithm (DCA)


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Clustering Based Upon Weight

Each node has arbitrary weight assigned Allow designer to choose nodes that arebetter suited for clusterhead role

Hand carried devices would have a lowerweight than vehicle carried devices

Clusterhead has largest generic weightin the neighborhood


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Desired Graph Properties

1) Every ordinary node has at least aclusterhead as neighbor (dominanceproperty)

2) Every ordinary node affiliates with theneighboring clusterhead that has thebigger weight

3) No two clusterheads can be neighbors(independence property)


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Assumptions

Same as first paper Author emphasis that sole knowledge ofthe topology local to each node


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Algorithm

At startup each node announces itsweightNodes with the highest weigh announcethat they are clusterheadsNodes with lower weights join clustersNode decides which role to assume onlywhen all its neighbors with biggerweights have decided their own roles


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Authors Evaluation

Easy to implementTime complexity

Changing topology of the ad hoc networkRather than size of the network


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Why They Are Wrong*

Weights would be difficult to assign aprioriNo reason to believe that algorithmachieved optimum placement

Could be local optima

No demonstration that algorithm worked

*terminology per Dr Cha

P t Di i


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Presenters Discussion Same

Node decides its own role (clusterhead orordinary node)

Knowing its current one hop neighbors As opposed to the knowledge of one and two hopneighbors as required by previous algorithms

Both algorithms are executed at each node Assumes nodes know identity of the one hopneighborsOrganizes network with same clusteringstructure

P t Di i


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Presenters Discussion Different

Paper 1Metric is smallest number of clustersEvaluation based upon creating clusters withthe largest possible number of nodesMetric calculated by nodes

Paper 2

Uses arbitrary weight assigned to each nodeWeight represents its ability to be a clusterhead

clustering in ad hoc networks

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