In Wireless Sensor Networks, sustaining lifetime is one of the significant challenges due to the energy-limited characteristics of sensor nodes which are deployed for the environment monitoring. To cope up with this challenge, clustering is the potential energy conservation method. In this context clustering protocols that use the merits of hybrid metaheuristic swarm intelligence algorithms with better algorithmic efficiency and potentiality of addressing all the above challenges is essential. In this paper, an Energy-Aware Self-Adaptive Differential Evoluted Bear Smell Optimization Algorithm (ESDEBSOA) with potential selection of Cluster Heads (CHs) and subsequent optimized clustering process is propounded for sustaining energy stability as well as network lifespan. This clustering mechanism integrates classical Differential Evolution (DE) with Bear Smell Optimization Algorithm (BSOA) for guaranteeing ideal CH selection and enhancing the optimization degree of the algorithm. This ESDEBSOA approach achieved efficient selection of CHs using fitness function that includes factors of residual energy, distance between sensors distance between CHs and sink, delay, Packet Drop Ratio (PDR), path loss, node centrality, restart number, link quality and node degree. The simulation experimental results confirmed better optimal cluster count of 11.56%, packet loss rate of 10.34%, end-to-end delay of 13.86% and reisual energy of 13.54%, compared to the baseline approaches.
Kalpana et al. (Wed,) studied this question.