Adaptive Localization Strategies for Underwater WSNs: The PSO-AUV Hybrid Model
摘要
Underwater Wireless Sensor Networks (UWSNs) play a crucial role in marine exploration, environmental monitoring, and maritime security. Localization of sensor nodes is crucial for maintaining data integrity, ensuring efficient communication, and facilitating effective tracking. However, the underwater environment presents a unique set of challenges, including signal attenuation, high latency, limited bandwidth, and the mobility of nodes, all of which demand innovative solutions. A comprehensive review of eco-friendly localization techniques in UWSNs highlights how researchers are addressing these challenges. The paper examines sustainable methods, including energy-efficient algorithms, cooperative positioning strategies, and bio-inspired models like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Additionally, it explores the potential of renewable energy sources, such as solar buoys and microbial fuel cells, to alleviate the energy constraints underwater nodes face. Moreover, the analysis extends to hybrid localization approaches that combine stationary anchors with mobile Autonomous Underwater Vehicles (AUVs). This integration aims to improve localization accuracy while also minimizing energy consumption. Importantly, the paper underscores the environmental implications of UWSNs, advocating for low-impact deployment strategies and green communication protocols to safeguard marine ecosystems. This paper aims to offer valuable insights into the development of sustainable localization frameworks that effectively balance precision, energy efficiency, and ecological preservation, providing guidance for future research and practical deployments in the field of UWSNs.