A distributed clustering algorithm for large-scale dynamic networks

We propose an algorithm that builds and maintains clusters over a network subject to mobility. This algorithm is fully decentralized and makes all the different clusters grow concurrently. The algorithm uses circulating tokens that collect data and move according to a random walk traversal scheme. T...

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Bibliographic Details
Published in:Cluster computing Vol. 15; no. 4; pp. 335 - 350
Main Authors: Bernard, Thibault, Bui, Alain, Pilard, Laurence, Sohier, Devan
Format: Journal Article
Language:English
Published: Boston Springer US 01.12.2012
Springer Nature B.V
Subjects:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Control algorithms
Control theory
Decentralized control
Nodes
Operating Systems
Processor Architectures
Random walk
Topology
Clustering
Random walks
Distributed computing
Distributed algorithms
ISSN:1386-7857, 1573-7543
Online Access:Get full text
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Summary:We propose an algorithm that builds and maintains clusters over a network subject to mobility. This algorithm is fully decentralized and makes all the different clusters grow concurrently. The algorithm uses circulating tokens that collect data and move according to a random walk traversal scheme. Their task consists in (i) creating a cluster with the nodes it discovers and (ii) managing the cluster expansion; all decisions affecting the cluster are taken only by a node that owns the token. The size of each cluster is maintained higher than m nodes ( m is a parameter of the algorithm). The obtained clustering is locally optimal in the sense that, with only a local view of each clusters, it computes the largest possible number of clusters (i.e. the sizes of the clusters are as close to m as possible). This algorithm is designed as a decentralized control algorithm for large scale networks and is mobility-adaptive: after a series of topological changes, the algorithm converges to a clustering. This recomputation only affects nodes in clusters where topological changes happened, and in adjacent clusters.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-011-0153-z