Multi-AUV Trajectory Learning for Sustainable Underwater IoT with Acoustic Energy Transfer

TL;DR

This paper introduces a multi-AUV framework using deep reinforcement learning to optimize trajectory control and acoustic energy transfer, enhancing sustainable underwater IoT operations.

Contribution

It presents a novel joint trajectory and communication control method for multiple AUVs using PPO, addressing energy and information freshness in underwater IoT.

Findings

Significant reduction in average Age of Information (AoI).

Improved fairness and data collection efficiency.

Scalable performance gains with increasing network size.

Abstract

The Internet of Underwater Things (IoUT) supports ocean sensing and offshore monitoring but requires coordinated mobility and energy-aware communication to sustain long-term operation. This letter proposes a multi-AUV framework that jointly addresses trajectory control and acoustic communication for sustainable IoUT operation. The problem is formulated as a Markov decision process that integrates continuous AUV kinematics, propulsion-aware energy consumption, acoustic energy transfer feasibility, and Age of Information (AoI) regulation. A centralized deep reinforcement learning policy based on Proximal Policy Optimization (PPO) is developed to coordinate multiple AUVs under docking and safety constraints. The proposed approach is evaluated against structured heuristic baselines and demonstrates significant reductions in average AoI while improving fairness and data collection efficiency. Results show that cooperative multi-AUV control provides scalable performance gains as the network size increases.