用AUV同时给水下设备供能并传数据，让它们永久运行。
AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization
- 用深度强化学习设计两类通信方案：高复杂度高性能的频分双工和低复杂度中等性能的时分双工。
- 相比基线方法，平均信息年龄降低，能量捕获量提升，数据采集公平性更好。
- 适合关注水下物联网长期运行、能量与信息协同传输的研究者。
水下物联网（IoUT）日益受到关注，旨在监测海洋生物与深海环境、进行水下监控及维护水下设施。然而，传统依赖电池供电的IoUT设备存在寿命短、废弃后污染环境等问题。本文提出一种可持续方案，通过自主水下航行器（AUV）同时实现从IoUT设备的信息上行传输和声能传输（AET），使设备可能无限期运行。为应对时延敏感性，采用信息年龄（AoI）和Jain公平指数作为评估指标。开发了两种深度强化学习（DRL）算法，分别提供高复杂度、高性能的频分双工（FDD）方案和低复杂度、中等性能的时分双工（TDD）方案。结果表明，所提的FDD与TDD方案显著降低了平均AoI，提升了捕获能量，并增强了数据收集的公平性，优于基线方法。
原文摘要 · Abstract (English)
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 paper 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.
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