An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Abstract

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network’s longevity and optimizes the routing. As a result of the study’s findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.