Memetic Particle Gravitation Optimization Algorithm-based Optimal Cluster Head Selection in Wireless Sensor Networks (WSNs)

Abstract

Abstract: Wireless Sensor Networks (WSNs) consist of millions of sensor nodes that operate cooperatively for attaining the objective of sensing and transmitting information to the base station for necessary decision making processes. In WSNs, the problem of hotspot or energy hole is a major issue that arises when the number of sensor nodes in close proximity of the base station decreases rapidly and results in a network partitioning. This issue of energy holes is feasible in the network only when the difference between the energy consumptions of the sensor nodes are quite large and which has the capability of minimizing the network lifespan. This limitation of WSNs needs to be handled through the potential selection of cluster heads with maximized energy efficiency. In this paper,  Memetic Particle Gravitation Optimization Algorithm-based Optimal Cluster Head Selection scheme is proposed for handling the issue of the energy hole  in order to sustain energy stability and prolonged network lifetime in WSNs. This MPGOA-OCHS scheme facilitates cluster head head selection by integrating the merits of Centralized Particle swarm Optimization (CPSO) and Gravitational search algorithm (GSA) in order to maintain balance between the rate of intensification and diversification in the cluster head selection process. The simulation results proved that the proposed MPGOA-OCHS scheme is predominant in residual energy by 22.21% and prolonged network lifetime by 16.39%, compared to the baseline schemes.