Optimizing Cluster Head Selection Algorithms to Improve Power Efficiency in Agricultural Wireless Sensor Networks

Abstract

The current research addresses the management of Wireless Sensor Networks (WSNs) with a focus on optimizing power consumption, a critical concern in current research. The study uses agricultural WSNs to monitor farming areas, aiming to enhance decision-making in farm management. The importance of such monitoring systems in the region is emphasized. Previous research highlights clustering as an effective technique for power optimization, although it faces challenges, notably the selection of cluster heads responsible for data transmission. The Whale Optimizing Algorithm is cited as an example of a cluster head selection algorithm, which uses a fitness function considering node residual energy and the total energy of adjacent nodes. This research will analyze these algorithms and propose enhancements, particularly focusing on energy balancing. Additionally, it will conduct a study of agricultural networks, gathering data from existing agricultural WSNs. The analysis phase will involve using simulation software, primarily Matlab, to simulate and evaluate the proposed agricultural monitoring system.