# AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization

arXiv: https://arxiv.org/abs/2601.08491 · HTML: https://mlsanity.com/paper/2601.08491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks](https://mlsanity.com/paper/1905.02993/) (2019)
- [Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge](https://mlsanity.com/paper/2301.03423/) (2023)
- [Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2309.14757/) (2023)
- [Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT, and Reinforcement Learning](https://mlsanity.com/paper/2302.10368/) (2023)
- [A Deep Reinforcement Learning Approach for Improving Age of Information in Mission-Critical IoT](https://mlsanity.com/paper/2311.13861/) (2023)

## Abstract (arXiv)

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.