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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