Underwater Data Collection Scheme based on LLMs

Underwater Wireless Sensor Networks (UWSNs) generate critical data for marine exploration and monitoring, requiring efficient data collection mechanisms to ensure both data integrity and freshness. Autonomous Underwater Vehicles (AUVs), with their superior mobility and ability to establish close-range, high-bandwidth communication links with sensor nodes, have been widely adopted as mobile collectors. However, existing approaches rely on pre-planned trajectories that cannot adapt to dynamic factors. In this paper, we propose an intelligent scheduling framework that leverages Large Language Models (LLMs) to enable adaptive multi-AUV data collection in dynamic underwater environments. In particular, an iterative optimization algorithm is designed to refine solutions through multiple reasoning cycles, achieving efficient scheduling performance. Moreover, we propose a dynamic evaluation mechanism to enable prompt detection and adaptation to environmental changes, and introduce LoRA-based fine-tuning for autonomous performance evolution by leveraging historical task execution data. Comprehensive experiments demonstrate that our approach achieves at least 77.4% improvement in collected value of information (VoI) compared to state-of-the-art methods under dynamic scenarios.