Overview

In recent years, wireless sensor networks (WSNs) have developed as a potentially useful technology for a wide variety of purposes, from surveillance to environmental monitoring. Wireless sensor networks are networks of tiny, low-power sensor nodes that can collect, analyze, and transmit data wirelessly. To gather information about their environments, these nodes generally have sensors like temperature, humidity, and motion detectors. The limited energy and computing resources of the sensor nodes are a significant barrier to WSNs. Since sensor nodes have limited energy capabilities due to their tiny size and dependency on batteries, energy efficiency is a crucial factor in WSN design. In order to keep WSNs up and running for as long as possible, it is essential that their lifespan be maximized while their energy consumption is kept to a minimum (Akyildiz et al., 2002). Many other energy-saving methods and procedures for WSNs have been offered by scientists as potential solutions to these problems. The goal of these methods is to reduce power consumption across the board, from individual sensor nodes to the network as a whole. In order to decrease the amount of energy needed to transmit data between sensor nodes, many clustering algorithms have been developed. Clustering is often employed in WSNs to increase scalability, decrease energy consumption, and consolidate data (Heinzelman et al., 2000). Clustering is a method for grouping sensor nodes, with the cluster head (CH) of each cluster being in charge of coordinating the work of the other nodes in the cluster (Heinzelman et al., 2002). The cluster heads gather, analyze, and send data from their clusters to the base station (BS), which helps save power and extend the life of the network.

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