BAFSA: Breeding Artificial Fish Swarm Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) is made up of a large number of independent nodes with sensors, wireless interfaces for communication with limited processing and energy resources. WSNs are used for distributed and cooperative sensing of events which are of interest in all fields of science. For efficient operation of sensor network data aggregation and transmission of data to the Base Station plays an important role and should be capable of adapting itself to the scenario under which it is deployed. To reduce the overall network energy consumption, the nodes are divided into clusters with one node acting as the Cluster Head (CH) to receive and aggregate the collected information. Clustering in sensor network is done to reduce the communication overhead and thereby improve the network performance and lifetime. An optimal selection of the CHs is an NP-hard problem, therefore, various metaheuristic based techniques have been proposed in the literature. This work proposed an optimized CH selection using an improved Artificial Fish Swarm Algorithm (AFSA) metaheuristic. Extensive simulations show the improved performance of the proposed protocol compared to other popular techniques including LEACH and Genetic Algorithm.