Smart pareto-optimized genetic algorithm for energy-efficient clustering and routing in wireless sensor networks

Healthcare, business, and the military employ wireless sensor networks (WSNs). Unfortunately, these networks have power supply, storage, and computing restrictions for sensor nodes. To overcome these difficulties, enhance energy efficiency, and extend network lifetime, we present a novel Pareto-based Genetic Algorithm for Energy-Efficient Clustering and Routing (PGAECR). It incorporates the best results from earlier networking sessions into the starting population for the present rounds, improving convergence speed and solution quality in the search process. The technique combines decisions about clustering and routing into one chromosome. A multi-objective fitness function that takes into account total energy consumption, residual energy balance, load distribution, and network longevity evaluates it. The first group comprises the best-performing solutions from the past, designed to aid convergence and enhance solution quality. An experimental examination examines factors such as transmission energy (ET, ER), data packet length, amplifier energy models (Efs, Emp), communication range, and node density across different network conditions. Experimental results indicate that PGAECR outperforms five other methods, demonstrating superior load balancing with minimal variance in cluster head loads across various scenarios. The proposed algorithm reduced energy usage by 12.4% and increased network longevity by 15.7% compared to conventional clustering and routing methods.