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Proceedings Paper

Particle swarm optimization for the clustering of wireless sensors
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Paper Abstract

Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network.

Paper Details

Date Published: 23 July 2003
PDF: 11 pages
Proc. SPIE 5100, Digital Wireless Communications V, (23 July 2003); doi: 10.1117/12.499080
Show Author Affiliations
Jason C. Tillett, Rochester Institute of Technology (United States)
Raghuveer M. Rao, Rochester Institute of Technology (United States)
Ferat Sahin, Rochester Institute of Technology (United States)
T. M. Rao, SUNY/Brockport (United States)


Published in SPIE Proceedings Vol. 5100:
Digital Wireless Communications V
Raghuveer M. Rao; Soheil A. Dianat; Michael D. Zoltowski, Editor(s)

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