Energy-Efficient Clustering in WSNs Using Multi-Objective Genetic Algorithms

This research proposes a Multiobjective Genetic Algorithm (MOGA) for energy efficient clustering in Wireless Sensor Networks (WSN). Clustering is a key tactic for improving network scalability and energy efficiency in WSNs since it lowers communication overhead and lengthens network lifetime. The proposed MOGA addresses this by optimizing several goals at once, such as improving network longevity, decreasing energy usage, and guaranteeing consistent energy distribution among sensor nodes. Under the guidance of a Pareto-based methodology, the algorithm evolves possible solutions using genetic operators including crossover, mutation, and selection in order to find a set of ideal trade-offs. The effectiveness of the proposed strategy is demonstrated by simulation results showing significant improvements in energy efficiency and network lifetime compared to conventional clustering methods. In large-scale WSNs, this method offers a reliable and expandable option for energy-efficient clustering. A MOGA was used to achieve a 93.5% efficiency.