GENETIC ALGORITHM OPTIMISED LEACH PROTOCOL FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS

Abstract

Research in wireless sensor networks (WSN) has traditionally prioritized characteristics such as extensive integration, energy efficiency, and energy consumption. Researchers are highly enthusiastic about energy-efficient solutions, since they have the potential to conserve energy in sensor-nodes in energy constraint environment. Conserving energy is a major obstacle that WSNs face, and it plays a critical role in determining the network's longevity. WSN protocols encompass several approaches, but clustering-based hierarchical routing protocols are particularly emphasized because of their enhanced scalability. Sensors, being battery-operated, often have constraints in terms of available energy, which is often fixed and cannot be altered in most cases. The Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is extensively employed as an energy-efficient solution for sensor networks. This study involved the development of an enhanced LEACH protocol using genetic algorithm (GA) optimization. Analytical study and simulations utilizing the MATLAB software environment were conducted to evaluate the performance of the proposed GAO-LEACH methodology. The assessment technique incorporated metrics such as latency, throughput, energy usage, and Packet Delivery Ratio. The simulation findings indicate that the suggested GAO-LEACH strategy has exhibited greater performance when compared to other techniques such as the classic LEACH method and Distributed Energy Efficient Clustering (DEEC) methodology. Comparing the GAO-LEACH technique with the regular LEECH protocol, the findings demonstrated significant improvements. The GAO-LEACH strategy exhibited a staggering 22.38% increase in throughput, 27.71% increase in Packet Delivery Ratio, 80.09% decrease in energy consumption rate, and 46.37% reduction in network latency.