A NOVEL AI-ASSISTED ENERGY EQUALIZATION AND COMPRESSION ENABLED ROUTING FRAMEWORK FOR WSNS

Wireless Sensor Networks (WSNs) have transformed monitoring and tracking applications across diverse domains. These networks, comprised of small sensor nodes transmitting data to a central base station (BS), face a significant challenge due to limited energy resources, impacting operation allongevity.This study addresses this challenge by proposing an innovative energy-efficient routing protocol. The primary aim is to develop an optimized routing model based on clusters, enhancing energy efficiency, through put, and reducing delay and dead nodes in the network. To achieve this, clustering with the k-means algorithm is employed, followed by the selection of cluster heads using PSO-mutation, optimizing for suitable cluster heads. Subsequently, the routing between cluster heads is optimized using the Golden Eagle algorithm(GEO),with benchmark comparison against Leach-CR. Leveraging the Golden Eagle algorithm, the protocol optimizes communication paths by intelligently selecting routes to minimize energy consumption. Motivated by the limitations of existing energy-efficient routing protocols, which struggle to comprehensively address diverse applications and energy constraints, this study proposes amore robust and adaptable routing protocol. Rigorous validation through extensive Matlab simulations evaluates metrics such as dead nodes, throughput, energy consumption, and network delay. Simulation results in Matlab show ssignificant enhancements over the benchmark model GA-PCT& Leach-CR routing protocol, demonstrating substantial energy savings and extended network lifespan.