Clustering optimisation using fuzzy c-means clustering and artificial bee colony algo-rithm for wireless sensor network
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Abstract
Wireless sensor networks (WSNs) play an important role in numerous applications such as industrial automation, commercial, robotics, environmental monitoring, landslide detection, earthquake detection, transport and logistics, and habitat monitoring. WSN clustering provides an efficient way to improve the network lifetime, throughput, scalability, and packet delivery ratio. However, the performance of WSN is limited because of low-power battery-operated sensor nodes and improper positioning of the cluster heads (CHs) during cluster formation. This study presents a fuzzy c-means algorithm (FCM) for WSN clustering and an artificial bee colony (ABC) algorithm for optimal selection of CHs. The proposed ABC considers various factors for clustering, such as the energy Gini coefficient, CH energy balancing, inter- and intra-cluster distance, and CH load balancing factors. The proposed algorithm provides optimised cluster selection that provides better network lifetime and throughput than traditional FCM.
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