AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization

2025-06-15

IEEE Internet of Things Journal

AI Summary

This work addresses the limited operational lifetime and environmental risks of conventional battery-powered underwater Internet of Things (IoT) devices by proposing a collaborative framework that integrates acoustic energy transfer and data collection via an autonomous underwater vehicle (AUV). The study introduces a joint optimization of AUV trajectory, Age of Information (AoI), and energy fairness, and for the first time applies deep reinforcement learning to simultaneously optimize underwater acoustic energy delivery and information timeliness. A dual-mode scheme combining frequency-division duplexing (FDD) and time-division duplexing (TDD) is designed to balance performance and computational complexity. Experimental results demonstrate that the proposed approach significantly reduces average AoI, enhances energy harvesting efficiency, and improves fairness in data collection, outperforming existing baseline methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Internet of Underwater Things (IoUT) is increasingly gathering attention with the aim of monitoring sea life and deep ocean environment, underwater surveillance as well as maintenance of underwater installments. However, conventional IoUT devices, reliant on battery power, face limitations in lifespan and pose environmental hazards upon disposal. This article introduces a sustainable approach for simultaneous information uplink from the IoUT devices and acoustic energy transfer (AET) to the devices via an autonomous underwater vehicle (AUV), potentially enabling them to operate indefinitely. To tackle the time-sensitivity, we adopt age of information (AoI), and Jain’s fairness index. We develop two deep-reinforcement learning (DRL) algorithms, offering a high-complexity, high-performance frequency division duplex (FDD) solution and a low-complexity, medium-performance time division duplex (TDD) approach. The results elucidate that the proposed FDD and TDD solutions significantly reduce the average AoI and boost the harvested energy as well as data collection fairness compared to baseline approaches.

Problem

• Internet of Underwater Things
• Acoustic Energy Transfer
• Age of Information
• Autonomous Underwater Vehicle
• Sustainable Operation

Innovation

• Acoustic Energy Transfer
• Age of Information
• Deep Reinforcement Learning
• Autonomous Underwater Vehicle
• IoUT