Energy-aware dynamic adjustment integrated kookaburra optimization based efficient routing in WSN

Abstract

In this paper a novel kookaburra optimization algorithm based dynamic adjustment strategy (KOA-DAS) method has been proposed in this paper for the energy efficient (EE) clustering and routing in wireless sensor network (WSN). The satin bowerbird optimization (SBO) is utilized for optimum cluster head (CH) selection. The proposed KOA-DAS model is utilized for an efficient routing through considering the fitness functions like distance from CH to base station (BS), remaining energy and intra-communication cost. The suggested framework has been assessed using a MATLAB simulator. The efficacy of the suggested KOA-DAS framework has been determined using evaluation metrics including execution time, average residual energy, network lifetime (NL), latency, packet delivery ratio (PDR), computation cost, energy consumption (EC), and alive nodes. The suggested KOA-DAS framework achieves the lowest energy efficiency by 23.44%, 19.31%, and 14.44% than the ASFO, EELCR, and K-LionER approaches. The proposed model effectively selects the CH and routing through dynamically adjusting parameters, which results in minimum EC and extending NL.