An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

Abstract

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented. It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS). The algorithm was analyzed in detail and proper swarm size, evolving generations, gene-exchange individual order, and gene-exchange proportion in molecule were obtained for better algorithm performances. According to the test results, the appropriate parameters are about 50 swarm individuals, over 3 000 evolving generations, 20%–25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals. The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement. It can reach a better result quickly, especially with the proper calculation parameters.

Keywords

wireless sensor network / deterministic area coverage / immune-swarm algorithm / particle swarm optimization / artificial immune system

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