Exploiting Deep Reinforcement Learning for Multi-AUV Assisted VoI-Maximum Data Collection in UWSNs

Reliable and timely data collection is an important and challenging problem for underwater wireless sensor networks (UWSNs), partly due to the very slow underwater communication and the difficulty in recharging the sensors. In this paper, we exploit the use of autonomous underwater vehicles (AUVs) for UWSN data collection task. Specifically, we investigate how to maximize the value of information (VoI) for data collection through joint optimization of cluster head (CH) selection and multi-AUV path planning. We formulate the joint optimization problem for the task, taking into account the energy constraints of sensor nodes. To solve the problem, we propose a deep reinforcement learning algorithm based on an encoder-decoder architecture. The entire UWSN system is fed into the encoder network, followed by a composite decoder consisting of an AUV selection decoder and a cluster access sequence decoder to obtain the cluster access sequence for each AUV. Based on the determined sequences, we further utilize the dynamic programming algorithm to achieve optimal CH selection. Finally, we obtain the sequence of AUVs accessing the selected CHs. Simulation results demonstrate that the proposed learning-based approach converges and achieves a higher VoI than the existing benchmark algorithms.