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CLUSTERING SCHEMES FOR MOBILE AD HOC NETWORK

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CLUSTERING SCHEMES FOR MOBILE AD HOC NETWORK Speaker Fu-Yuan Chuang Advisor Ho-Ting Wu Date 2006.04.25 Outline Introduction Clustering Scheme Overview ... – PowerPoint PPT presentation

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Title: CLUSTERING SCHEMES FOR MOBILE AD HOC NETWORK


1
CLUSTERING SCHEMES FOR MOBILE AD HOC NETWORK
  • SpeakerFu-Yuan Chuang
  • AdvisorHo-Ting Wu
  • Date2006.04.25

2
Outline
  • Introduction
  • Clustering Scheme Overview
  • Classifying Clustering Schemes
  • DS-based clustering
  • Wus CDS Algorithm
  • Chens WCDS Algorithm
  • Summary of DS-based Clustering

3
Introduction
  • Dynamic routing is the most important issue in
    MANETs
  • A flat structure encounters scalability problem
  • Proactive routing protocols is O(n2)
  • Reactive routing sheme
  • RREQ flooding over the whole network
  • Route setup delay
  • A hierarchical architecture

4
Clustering Scheme Overview
  • Virtual group
  • Clusterhead
  • a local coordinator, performing intra-cluster
    transmission arrangement, data forwarding
  • Clustergateway
  • non-clusterhead node with inter-cluster links
    access neighboring clusters, forward information
    between clusters
  • Clustermember
  • ordinary node, non-clusterhead node without any
    inter-cluster links

5
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6
Three Benefits
  • spatial reuse of resources to increase the system
    capacity
  • the same frequency or code set
  • routing
  • The generation and spreading of routing
    information can be restricted in the set of
    clusterheads and clustergateways
  • an ad hoc network appear smaller and more stable
    in the view of each mobile terminal
  • when a mobile node changes its attaching cluster,
    only nodes residing in the corresponding clusters
    need to update the information

7
The cost of clustering (1/3)
  • Explicit control message for clustering
  • Clustering requires explicit clustering-related
    information exchanged between node pairs
  • Ripple effect of re-clustering
  • The re-election of a single clusterhead may
    affect the cluster structure of many other
    clusters and completely alter the cluster
    topology over the whole network

8
The cost of clustering (2/3)
  • Stationary assumption for cluster formation
  • Assume that mobile nodes keep static when cluster
    formation is in progress
  • Constant Computation round
  • Computation round is the number of rounds that a
    cluster formation procedure

9
The cost of clustering (3/3)
  • Communication complexity
  • The total amount of clustering-related message
    exchanged for the cluster formation

10
Classifying Clustering Schemes(1/3)
  • DS-based clustering
  • Finding a (weakly) connected dominating set to
    reduce the number of nodes participating in route
    search or routing table maintenance
  • Low-maintenance clustering
  • Providing a cluster infrastructure for upper
    layer applications with minimized
    clustering-related maintenance cost

11
Classifying Clustering Schemes(2/3)
  • Mobility-aware clustering
  • Utilizing mobile nodes mobility behavior for
    cluster construction and maintenance and
    assigning mobile nodes with low relative speed to
    the same cluster to tighten the connection in
    such a cluster
  • Energy-efficient clustering
  • Avoiding unnecessary energy consumption or
    balancing energy consumption for mobile nodes in
    order to prolong the lifetime of mobile terminals
    and a network

12
Classifying Clustering Schemes(3/3)
  • Load-balancing clustering
  • Distributing the workload of a network more
    evenly into clusters by limiting the number of
    mobile nodes in each cluster in a defined range
  • Combined-metrics-based clustering
  • Considering multiple metrics in cluster
    configuration, including node degree, mobility,
    battery energy, cluster size

13
DS-based clustering
  • A dominating set of a graph G (V, E) is a vertex
    subset S?V , such that every vertex v?V is either
    in S or adjacent to a vertex of S
  • A connected dominating set (CDS) of a graph G is
    a dominating set whose induced graph is connected

14
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15
DS-based clustering(cont.)
  • Table-driven routing
  • Only codes in the CDS are required to construct
    and maintain the routing tables
  • On-demand routing
  • The route search space is limited to the CDS
  • To keep a DS connected and with approximately
    minimum size is not a trivial task

16
DS-based clustering Algorithm Wus CDS Algorithm
  • Marking Process
  • To find CDS
  • Prune redundant nodes from CDS
  • To reduce the size of CDS

17
Marking Process
  • Define a network as a graph G (V,E)
  • Initially, all nodes are unmarked
  • Every v exchanges its N(v) with all its neighbors
  • Mark v if there exists 2 unconnected neighbors

18
Example
  • A B C E

Open neighbors set of all nodes N(A)
B,D N(B) A,C,D N(C) B, E N(D) A,
B N(E) C
D
After step 2 A N(B), N(D) B N(A), N(C),
N(D) C N(B), N(E) D N(A), N(B) E N(C)
19
Prune redundant nodes from CDS
  • Assign a distinct id, id(v) to each vertex v in G
  • Define Nv as a closed neighbor set of v

20
Prune redundant nodes from CDS
  • Rule 1 Considers two vertices v and u in G. If
    Nv Nu in G, and id(v) lt id(u), change
    the marker of v to F if node v is marded

21
Prune redundant nodes from CDS
  • Rule 2 Assume u and w are two marked neighbors
    of marked vertex v in G. If N(v) N(u) U N(w)
    in G and id(v) minid(v), id(u), id(w), then
    unmark v.

22
DS-based clustering Algorithm Chens WCDS
Algorithm
  • Reduce the number of clusters by relaxing the
    connectivity requirement
  • The subgraph weakly induced by S(S?V) is the
    graph ltSgtw(N S, E n (N SS)).
  • ltSgtw includes the vertices in S and all of their
    neighbors as vertex set
  • The edges of ltSgtw are all edges of G which have
    at least one end point in S

23
Weakly induced subgraph (example)
Vertex set black vertices Edge set black lines
24
Weakly-connected dominating set
  • A vertex subset S is a weakly-connected
    dominating set (WCDS), if S is a dominating set
    and ltSgtw is connected

25
Algorithms for finding small WCDS
  • Algorithm I and II Two centralized algorithms
  • Algorithm III and IV Distributed Implementations
    of Algorithm I and II
  • Algorithm V Distributed Asynchronous Approach

26
Chens WCDS Algo I (overview)
  • Given a graph G(V,E), each vertex is associated
    with a color (white, gray, or black)
  • All vertices are initially colored white
  • In each iteration, the algorithm color a white or
    gray vertex black and all its neighboring white
    vertices gray
  • At the end, the black vertices form a
    weakly-connected dominating set

27
Term piece
  • Piece refers to a particular substructure of the
    graph
  • A white piece is simply
  • a white vertex
  • A black piece contains a
  • maximal set of black
  • vertices whose weakly
  • induced subgraph is
  • connected plus any
  • adjacent gray vertices

The pieces are indicated by dotted regions
28
Term improvement
  • The improvement of a (non-black) vertex u is the
    number of pieces that would be merged into a
    single black piece if u were to be dyed black
  • In last example, dying vertex 5 black would merge
    4 piece, while dying vertex 4 would merge 3
    pieces

29
Chens WCDS Algo I(detail)
  • In each iteration, the algorithm choose a single
    white or gray vertex to dye black
  • The vertex is chosen greedily a vertex with
    maximum improvement is chosen
  • Until there is only one piece left

30
Initially, all nodes are white
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First Iteration
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Second Iteration
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Third Iteration
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Fourth Iteration
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Last Iteration
40
Summary of DS-based Clustering
41
Summary of DS-based Clustering
42
References
  • J. Y. YU and P. H. J. CHONG, "A Survey of
    Clustering Schemes for Mobile Ad Hoc Networks,"
    IEEE Communications Surveys and Tutorials, First
    Quarter 2005, Vol. 7, No. 1, pp. 32--48.
  • J. Wu and H. L. Li, On Calculating Connected
    Dominating Set for Efficient Routing in Ad Hoc
    Wireless Networks, Proc. 3rd Intl. Wksp.
    Discrete Algorithms and Methods for Mobile Comp.
    and Commun., 1999, pp. 714
  • Y.-Z. P. Chen and A. L. Liestman, Approximating
    Minimum Size Weakly-Connected Dominating Sets for
    Clustering Mobile Ad Hoc Networks, in Proc. 3rd
    ACM Intl. Symp. Mobile Ad Hoc Net. Comp., June
    2002, pp. 16572.
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