Encoder-Decoder Based Deep Reinforcement Learning for Multi-AUV Assisted Data Collection in UWSNs

Reliable and timely data collection poses a significant challenge for underwater wireless sensor networks (UWSNs), primarily due to the extremely low data rate of underwater communication and the difficulty in supplying energy to the sensor nodes. In this paper, we explore the use of multiple autonomous underwater vehicles (AUVs) for sequential data collection from sensor nodes distributed over a large area. To facilitate efficient data collection, appropriate cluster heads (CHs) are selected in each region to collect and aggregate data from nearby energy-limited sensor nodes, and then forward the data to the mobile AUVs. Specifically, we investigate how to maximize the value of information (VoI) of the collected data through the joint optimization of CH selection and multi-AUV path planning, while accounting for the energy constraints of sensor nodes. To tackle the formulated VoI maximization problem, we propose a deep reinforcement learning algorithm based on an encoder-decoder architecture. The whole 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 generate 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 that access the selected CHs. Simulation results demonstrate that the proposed learning-based approach converges and achieves a higher VoI than existing benchmark algorithms.