Self-Adaptive Cuckoo Search-Based Cluster Head Selection for Maximizing Network Lifetime in Wireless Sensor Networks

Abstract

Energy stabilization is considered as the core factor of concern in wireless sensor networks (WSNs) as they are responsible for lifetime longevity of sensor nodes leading to maximized network lifetime. In this context, novel metaheuristic algorithm is highly ideal for facilitating clustering process and subsequent cluster head selection process. In this paper, a self-adaptive cuckoo search-based cluster head selection (SACS-CHS) scheme is proposed for maximizing network lifetime with sustained energy stability of sensor nodes. This SACS-CHS scheme is proposed with adaptive parameters that attributes toward better cluster head selection without the need of tuning the utilized parameters. It included the reduced population proportion concept based on the fitness evaluated based on the previous and current best solution. This CH selection is attained through the fitness function formulated based on residual energy, intra-cluster distance, and inter-cluster distance. It also incorporated Gaussian sampling mechanism that improved the tendencies of exploitation and exploration. In addition, Weibull distributed probability switching is used for increasing the trade-off between exploitation and exploration. The simulation results confirmed better energy stability of 12.38% and improved network lifetime of 14.21%, excellent to the baseline schemes considered for investigation.