Multiple Autonomous Underwater Vehicles-Assisted Data Collection in 6G-Driven Underwater Wireless Networks Based on Software-Defined MARL

The multiple Autonomous Underwater Vehicle (AUV)-assisted cooperative system or the AUV-based Underwater Ad-hoc Networks (UAN) system has been considered as a highly-potential future in underwater data surveillance. In this paper, we propose grid-based distributed data collection architecture and define two categories of navigation modes. Based on the proposed data collection model, we propose MADAC, a scheme based on AUV-based UAN to cooperatively collect data from 6G-driven underwater wireless networks. We utilize the Software-Defined Networking (SDN) technique to re-organize the architecture of AUV-based UAN and propose software-defined actor-critic MARL framework. Based on the proposed MARL framework, we present the paradigm of MADDPG algorithm with optimal similarity attention mechanism (MADDPG-SA), to plan the paths for the AUV-based UAN, especially the cooperative underwater obstacle avoidance, the task distribution balancing, the Value of Information (VoI) are concurrently taken into account. In particular, the proposed MADDPG-SA improves the running efficiency of the proposed MADDPG-SA by encouraging the agent to learn from the similar and better-performance agent. The evaluation results demonstrate that the proposed MADAC can schedule the AUV-based UAN to perform efficient underwater data collection, reduce data collection time and energy consumption, and balance data collection tasks in the AUV-based UAN.