A Comprehensive Survey of Next-Generation Optimization Algorithms for Target Coverage in Mobile Wireless Sensor Networks

In recent years, Wireless Sensor Networks (WSNs) have gained attention due to their real-time monitoring capabilities. These networks use low-power devices to collect and transmit data, becoming significant with the rise of 5G and the Internet of Things (IoT). Initially used for military purposes, WSNs have expanded into various sectors, particularly in smart agriculture, where they enhance efficiency through modern technology. By providing real-time data, WSNs help farmers optimize yields, reduce waste, and improve productivity, supporting the digital transformation of agriculture. Despite their advantages, WSNs face challenges such as routing, localization, energy efficiency, and coverage. This study provides a comprehensive survey of the coverage optimization problem in WSNs, focusing on meta-heuristic algorithms such as the Gray Wolf, Whale Swarm, Flower Pollination, and Cuckoo Algorithms. These algorithms are analyzed based on metrics like maximum coverage rate, energy consumption, and solution time. The survey highlights their potential to address challenges in WSN applications, particularly in agriculture and other domains, by optimizing sensor placement and improving network efficiency.