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.

翻译：水下物联网（IoUT）正日益受到关注，其目标在于监测海洋生物与深海环境、实施水下监视以及维护水下设施。然而，依赖电池供电的传统IoUT设备存在使用寿命有限的问题，且在废弃后会对环境构成危害。本文提出了一种可持续的方法，通过自主水下航行器（AUV）同时实现从IoUT设备的信息上行链路传输以及向这些设备的声学能量传输（AET），从而可能使设备实现无限期运行。为应对信息的时间敏感性，我们采用了信息年龄（AoI）和Jain公平性指数。我们开发了两种深度强化学习（DRL）算法，分别提供了一种高复杂度、高性能的频分双工（FDD）解决方案和一种低复杂度、中等性能的时分双工（TDD）方法。结果表明，与基线方法相比，所提出的FDD和TDD解决方案显著降低了平均AoI，并提升了能量收集效率以及数据收集的公平性。