MECOS: Cooperative Multi-UAV-Assisted Cross-Boundary Maritime Data Collection Leveraging MARL and LLM

作者：Yang Zhao, Hanjiang Luo, Hang Tao, Jingjing Wang, Jiehan Zhou, Kaishun Wu · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3642563 · 被引用次数：1 · 研究领域：UAV Applications and Optimization、Underwater Vehicles and Communication Systems、IoT and Edge/Fog Computing

The direct cross-boundary communication between Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) is a pivotal component in establishing the 6G integrated air-sea-space network, holding significant importance for applications such as marine data collection and maritime collaborative search and rescue. Nevertheless, existing solutions exhibit pronounced deficiencies in path planning efficiency, the coverage range of wireless optical communication, and edge computing load balancing, which result in a high Age of Information (AoI), severely compromising the performance of time-sensitive maritime missions. To address these challenges, this paper proposes a maritime data collection scheme called MECOS, which includes LMAR2P algorithm for UAVs path planning and MAPBal algorithm for UAVs to deal with the load balancing issue. In LMAR2P, a multi-agent deep reinforcement learning (MARL) architecture is adopted, in which we leverage the global understanding capability of large language models (LLMs) to provide state representation for MARL, in order to improve path planning efficiency. Furthermore, to solve the unbalanced computational load problem, we design a kullback-leibler (KL) divergence-based reward correction mechanism and propose a distributed adaptive offloading balancing algorithm MAPBal, which enables resource-aware task allocation to ensure load balancing and reduce data processing latency. The simulation results indicate that the MECOS scheme reduc...