We propose a new application of the optimization technique known as particle swarm optimization (PSO) to the problem of clustering nodes. The PSO approach is 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. In a typical optimization, some function or fitness is used as a criterion for the optimization. Here we use application specific criteria, where we are equalizing the number of nodes, and candidate cluster-heads in each cluster, with the objective of minimizing the energy expended by the nodes while maximizing the total data gathered. The objective criteria fit with the implementation of a wireless, ad hoc, sensor network with cluster-head routing and data aggregation.

Date of creation, presentation, or exhibit



Copyright 2002 IEEE Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ISBN: 0-7803-7569-6Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Microelectronic Engineering (KGCOE)


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