TY  - EJOU
AU  - Alkanhel, Reem 
AU  - Chinnathambi, Kalaiselvi 
AU  - Thilagavathi, C. 
AU  - Abouhawwash, Mohamed 
AU  - duailij, Mona A. Al 
AU  - Alohali, Manal Abdullah 
AU  - Khafaga, Doaa Sami 

TI  - An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 36
IS  - 2
SN  - 2326-005X

AB  - 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.
KW  - Clustering; energy consumption; genetic algorithm; multi swarm optimization; adaptive hierarchical clustering; routing; cluster head

DO  - 10.32604/iasc.2023.033430