Selective activation for wireless sensor network with minimum interference

Abstract

Wireless Sensor Networks (WSNs) have become increasingly newlineimportant in various real-time applications, such as environmental monitoring, newlinedisaster management, military surveillance, and healthcare. They enable efficient newlinedata collection, analysis, and decision-making, improving situational awareness, newlineresource allocation, and system performance. However, the efficient operation of newlinewireless sensor networks faces challenges related to coverage, connectivity, and newlineinterference. The proposed algorithms aim to achieve several key objectives in newlineWireless Sensor Networks (WSNs). The proposed algorithms have six main newlineobjectives: maximizing coverage, ensuring connectivity, minimizing coverage newlineoverlap, selecting nodes with higher residual energy, minimizing active sensor newlinenodes, and minimizing interference. These objectives aim to enhance coverage newlinequality, connectivity, energy efficiency, and interference management in wireless newlinesensor networks. newlineIn this research, three nature-inspired optimization algorithms are newlineintroduced for WSNs. The first algorithm, multi-objective randomized Grasshopper newlineOptimization Algorithm-based Selective Activation (MORGOA-SA), draws newlineinspiration from the behavior of grasshoppers. The second algorithm, Multi-Objective newlineAdaptive Horse Herd Optimization Algorithm-based Selective Activation newline(MOAHOA-SA), mimics the adaptive behavior of horse herds. The third newlinealgorithm, Multi-objective Chaotic Learning based Red Fox Selective Activation newlinealgorithm (MOCL-RFSA), incorporates the intelligent foraging behavior of red newlinefoxes. These algorithms leverage the unique characteristics and behaviors of their newlinerespective inspirations to optimize coverage, connectivity, and interference. newline