Cluster-head identification in ad hoc sensor networks using particle swarm optimization

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.