A Multi-AUV Collaborative Ocean Data Collection Method Based on LG-DQN and Data Value.

As a result of the development of the Internet of Underwater Things (IoUT), underwater connected devices generate a large volume of data with varying values and time sensitivity. Previous data collection strategies cannot accommodate the varying time requirements of various data types. To address the aforementioned issues, this paper proposes a cooperative data collection method (MADC-DV) for multiple autonomous underwater vehicles (AUVs) based on local global deep Q learning (LG-DQN) and data value, which divides data into emergency and non-emergency and achieves hybrid data collection. First, the MAC protocol for communication between AUVs and clusters is designed to divide non-emergency data into high-value data and low-value data, with low-value data not needing to reply to ACK acknowledgment packets, thereby reducing the non-emergency data collection delay. Second, non-emergency data are collected cooperatively using multiple AUVs, and the LG-DQN approach is used to plan the paths for multiple AUV data collection in order to reduce the overall energy consumption of underwater wireless sensor networks (UWSNs). Finally, emergency data are collected using a multi-hop routing approach to assist in the collection. A routing method is proposed to compensate for the inability of AUVs to be applied to emergency data collection. The experimental results indicate that the method can improve the network life cycle by 18.7%, reduce the delay in the collection of non-emergency data by 40%, and reduce the delay in the collection of emergency data by 26.3%, thereby meeting the varying time requirements for different types of data.