Sustainable Marine Surveillance Sensor Network Aided by SWIPT-Enabled AUVs

Oceans are vital for Earth's climate stability, oxygen production, and as sources of food and energy for countless organisms. However, human-induced climate change significantly disrupts marine ecosystems, emphasizing the need for advanced underwater monitoring. The Internet of Underwater Things (IoUT), composed of marine sensor networks, offers a promising solution, nonetheless, challenges such as limited communication range and constrained power supplies. To address these issues, this work proposes using simultaneous wireless information and power transfer (SWIPT) from autonomous underwater vehicles (AUVs) to enhance sensor node efficiency. We formulate an integer linear programming (ILP) problem aimed at optimizing AUV trajectories through marine sensor networks, minimizing propulsion energy and mission duration while ensuring adequate energy harvesting at each node. The problem is proven to be NP-hard, resembling the well-known traveling salesman problem (TSP). Further, we introduce CLEAR, a multi-agent deep-Q-network (DQN) framework that effectively selects optimal path for AUV-based data muling. Experimental results demonstrate that CLEAR significantly improves network energy-efficiency, reduces mission duration, boosts harvested energy, and decreases age-of-information (AoI) of sensor data.