An Improved Method Of Cluster Head Selection Using Machine Learning In Wsn

Abstract

Wireless Sensor Networks (WSNs) are comprised of numerous small, low-cost, and energy-limited sensor nodes that gather information from their immediate surroundings and relay it to a sink node. The utilisation of cluster-based routing protocols has been widely employed in Wireless Sensor Networks (WSNs) with the aim of enhancing network efficacy and extending network longevity. In the context of Wireless Sensor Networks (WSNs) that are cluster-based, the network is partitioned into clusters, with each cluster being assigned a Cluster Head (CH) responsible for data aggregation and forwarding to the sink node. The process of selecting Cluster Heads (CHs) is of utmost importance for optimising the performance of a network in terms of energy efficiency, network longevity, and communication overhead.