clustering schemes for mobile ad hoc network
DESCRIPTION
CLUSTERING SCHEMES FOR MOBILE AD HOC NETWORK. Speaker : Fu-Yuan Chuang Advisor : Ho-Ting Wu Date : 2006.04.25. Outline. Introduction Clustering Scheme Overview Classifying Clustering Schemes DS-based clustering Wu’s CDS Algorithm Chen’s WCDS Algorithm Summary of DS-based Clustering. - PowerPoint PPT PresentationTRANSCRIPT
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CLUSTERING SCHEMES FOR MOBILE AD HOC NETWORK
Speaker: Fu-Yuan ChuangAdvisor: Ho-Ting WuDate: 2006.04.25
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Outline
Introduction Clustering Scheme Overview Classifying Clustering Schemes DS-based clustering
Wu’s CDS AlgorithmChen’s WCDS Algorithm
Summary of DS-based Clustering
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Introduction
Dynamic routing is the most important issue in MANETs
A flat structure encounters scalability problem Proactive routing protocols is O(n^2) Reactive routing sheme:
RREQ flooding over the whole network Route setup delay
A hierarchical architecture
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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 neigh
boring clusters, forward information between clusters
Clustermember ordinary node, non-clusterhead node without any inter-clus
ter links
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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 nod
es residing in the corresponding clusters need to update the information
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The cost of clustering (1/3)
Explicit control message for clustering Clustering requires explicit clustering-related informati
on 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
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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
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The cost of clustering (3/3)
Communication complexity The total amount of clustering-related message
exchanged for the cluster formation
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Classifying Clustering Schemes(1/3)
DS-based clusteringFinding a (weakly) connected dominating set to
reduce the number of nodes participating in route search or routing table maintenance
Low-maintenance clusteringProviding a cluster infrastructure for upper layer
applications with minimized clustering-related maintenance cost
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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
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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
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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
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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
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DS-based clustering AlgorithmWu’s CDS Algorithm Marking Process
To find CDS Prune redundant nodes from CDS
To reduce the size of CDS
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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 neig
hbors Mark v if there exists 2 unconnected neigh
bors
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Example
A B C E
D
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}
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)
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Prune redundant nodes from CDS Assign a distinct id, id(v) to each vertex v i
n G Define N[v] as a closed neighbor set of v
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Prune redundant nodes from CDS Rule 1: Considers two vertices v and u in G’.
If N[v] N[u] in G, and id(v) < id(u),change the marker of v to F if node v is marded
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Prune redundant nodes from CDS Rule 2: Assume u and w are two marked neighb
ors of marked vertex v in G’. If N(v) N(u) U N(w) in G and id(v) = min{id(v), id(u), id(w)}, then unmark v.
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DS-based clustering AlgorithmChen’s WCDS Algorithm Reduce the number of clusters by relaxing the
connectivity requirement The subgraph weakly induced by S(S⊆V) is the
graph <S>w=(N [S], E ∩ (N [S]×S)). <S>w includes the vertices in S and all of their
neighbors as vertex set The edges of <S>w are all edges of G which have
at least one end point in S
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Weakly induced subgraph (example)
Vertex set: black vertices
Edge set: black lines
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Weakly-connected dominating set
A vertex subset S is a weakly-connected dominating set (WCDS), if S is a dominating set and <S>w is connected
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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
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Chen’s 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
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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
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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
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Chen’s 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
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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
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Summary of DS-based Clustering
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Summary of DS-based Clustering
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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 Int’l. Wksp. Discrete Algorithms and Methods for Mobile Comp. and Commun., 1999, pp. 7–14
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 Int’l. Symp. Mobile Ad Hoc Net. & Comp., June 2002, pp. 165–72.