Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world intelligent networks, they essentially use wireless sensor network (WSN) as perception layer to collect the intended data. This data is processed information send cloud servers through base station, challenge here consumption minimum energy for processing communication. dynamic formation cluster heads aware clustering schemes help improving lifetime WSNs. In recent years, grey wolf optimization (GWO) became most popular feature selection optimizing, swarm intelligent, robust metaheuristics algorithm that gives competitive results with impressive characteristics. spite several studies literature enhance performance GWO algorithm, there need further improvements terms selection, accuracy, execution time. this paper, we have proposed an energy-efficient head using improved version (EECHIGWO) alleviate imbalance between exploitation exploration, lack population diversity, premature convergence basic algorithm. primary goal paper efficiency, average throughput, stability, WSNs optimal EECHIGWO It considers sink distance, residual energy, balancing factor, intra-cluster distance parameters selecting head. EECHIGWO-based protocol has been tested number dead nodes, consumption, operating rounds, throughput. simulation confirmed resolved convergence, enhanced by levels Using improvement stability 169.29%, 19.03%, 253.73%, 307.89%, 333.51% compared SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, FIGWO protocols, respectively.