A Generalizable Attention-Based Data Collection Scheme for Multi-AUV Underwater Wireless Sensor Networks

Autonomous Underwater Vehicles (AUVs) provide a new prospect for data collection in underwater wireless sensor networks (UWSNs). For dynamic underwater environments, researchers typically apply deep reinforcement learning (DRL) to design multi-AUV collection schemes for UWSNs. However, these methods suffer from the following issues. 1) Overloaded observations. The importance of various observations for an AUV varies over time. Considering all observations equally complicates decision-making for subsequent actions. 2) Dynamic scale of AUVs and sensors. Once the number of AUVs or sensors is changed, traditional static neural networks require retraining, lacking scalability across diverse scenarios. To solve the above issues, we propose a Generalizable Attention-based Data collection scheme (GAMD) for Multi-AUV UWSNs, while enhancing AUVs’ collection efficiency. GAMD incorporates the attention mechanism with multi-agent DRL framework, which enables AUVs to prioritize observations more critical for action decisions. Moreover, we propose an adaptive information processing approach, enabling the AUV policy model to seamlessly adapt to various scenarios without retraining. Additionally, we develop a training paradigm with incremental complexity across different scale scenarios to simplify training process and accelerate convergence. Simulation results demonstrate that GAMD alleviates the training cost compared to the state-of-the-art methods, and simultaneously optimizes collection energy efficiency, collection time, and trajectory distance.