Chaotic Grey Wolf Optimization for Energy-Efficient Clustering and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs), the imperative challenge of energy efficiency takes center stage. This paper delves into inventive methodologies to combat energy depletion in sensor nodes, introducing the Chaotic Grey Wolf Optimization Algorithm (CGWOA) for optimal Cluster Head (CH) selection in WSNs. An evolution strategy and a mutation operation operator, CGWOA aims to amplify energy efficiency, scalability, and minimize network overhead through streamlined clustering. The outcomes underscore CGWOA's potential as a solution for advancing energy-efficient clustering and routing in WSNs, particularly in optimizing CH selection. The ensuing numerical validation underscores CGWOA's revealing an energy consumption of 1100 J, a throughput ratio of 69,785 kbps, 4900 active nodes, and a network lifetime extension to 8021. This comparative analysis shows the emphasis on problems to overcome existing methods like Particle Swarm Optimization for Energy-Efficient Clustering (PSO-EEC), Adaptive Hybrid Clustering Scheme with Grey Wolf Optimization (AHCS-GWO), Hybrid Optimization for Cluster Head Selection (HOCK).