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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks
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DOI：10.32604/iasc.2023.033430.png)
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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 enhan-cing the power feature in WSN. In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network's lifespan and routing opti-mization. By using distributed data transmission modification, an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption. 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 pro-posed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan com-putation reduces average packet loss, and end-to-end delay.
Keyword:
Clustering
energy consumption
genetic algorithm
multi swarm optimization
adaptive hierarchical clustering
routing
cluster head
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