Detecting Communities in Sparse MANETs

In sparse mobile ad hoc networks, placement of services and data is crucial to assure their availability to all nodes because sparse population of nodes can lead to (frequent) network partitions. If these dynamic networks display a fairly stable cluster structure, it is possible to utilize this stru...

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Bibliographic Details
Published in:IEEE/ACM transactions on networking 2011-10, Vol.19 (5), p.1434-1447
Main Authors: Drugan, O. V., Plagemann, T., Munthe-Kaas, E.
Format: Article
Language:English
Subjects:
Ad hoc networks
Clustering
Clustering algorithms
Clusters
Communities
community detection algorithm
Computer networks
Dynamics
Mobile computing
Network topology
Networks
rescue operations
Routing (telecommunications)
Routing protocols
sparse mobile ad hoc networks (MANETs)
Technological planning
Topology
wireless communications
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Summary:In sparse mobile ad hoc networks, placement of services and data is crucial to assure their availability to all nodes because sparse population of nodes can lead to (frequent) network partitions. If these dynamic networks display a fairly stable cluster structure, it is possible to utilize this structure to improve service and data availability. However, clustering in a dynamic network is a very challenging task due to the ever-changing topology and irregular density of such a network. In this paper, we investigate clustering of dynamic networks with the help of community detection mechanisms, using only topology information from the local routing table. The main aim of our approach is to reduce to "zero" the communication overhead needed for cluster management and to dynamically adapt to the size and layout of the network. We have performed extensive experiments to evaluate the consistency, quality, and stability of the clustering returned by our algorithms. The results show that our nonintrusive clustering indeed discovers temporary groups of nodes that form stable clusters in the network. Moreover, even though the local routing tables in general reflect slightly diverging topologies, our results still show only small differences between the communities detected at different nodes.
ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2011.2112376