The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors.

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.

With the rapid development of underwater materials technology and underwater robot technology, human exploitation of marine resources has been increasingly advanced, which has given rise to various application scenarios for Autonomous Underwater Vehicle (AUV) cluster networks, such as cooperative data collection and target tracking. In this paper, we aim to explore how to utilize networking and swarm intelligence to improve the AUV cluster network’s target tracking performance in a time-saving manner. Specifically, on account of our previous work, we introduce an underwater interrupted mechanism and propose an Interrupted Software-Defined Multi-AUV Reinforcement Learning (ISD-MARL) architecture. For MARL algorithm in ISD-MARL, we propose a time-saving MARL algorithm, S-MADDPG, integrating our proposed action optimization model and action network loss function, to accelerate the convergence of the MARL algorithm. Furthermore, to further improve the AUV cluster network’s path planning performance during the target tracking, we propose an Interrupted Tracking Path Planning Scheme (ITPPS) for the AUV cluster network based on the proposed ISD-MARL and S-MADDPG. The evaluation results showcase that our proposed scheme can effectively plan the underwater target tracking path for the AUV cluster network in a shorter time and outperform various mainstream strategies in terms of convergence speed and training time, etc.