Towards a sustainable Internet-of-Underwater-Things based on AUVs, SWIPT and reinforcement learning

Omeke, K., Mollel, M. , Shah, S. , Zhang, L. , Abbasi, Q. and Imran, M. A. (2024) Towards a sustainable Internet-of-Underwater-Things based on AUVs, SWIPT and reinforcement learning. IEEE Internet of Things Journal, 11(5), pp. 7640-7651. (doi: 10.1109/JIOT.2023.3319250)

Abstract

Life on Earth depends on healthy oceans, which supply a large percentage of the planet’s oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet-of-underwaterthings (IoUTs), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision-making. The sensors in conventional IoUTs are battery-powered, which limits their lifetime, and constitutes environmental hazards when they die. In this paper, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximising throughput and harvested power as a Markov Decision Process (MDP), and develop a model-free reinforcement learning (RL) solution. The model’s reward function incentivises the AUV to find optimal trajectories that maximise throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

• Item Type: Articles
• Additional Information: This work was supported by the Petroleum Technology Development Fund (PTDF) of the Federal Republic of Nigeria [grant number 1353/18].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Professor Lei and Imran, Professor Muhammad and Omeke, Dr Kenechi and Mollel, Dr Michael and Abbasi, Professor Qammer and Shah, Mr Syed
• Authors: Omeke, K., Mollel, M., Shah, S., Zhang, L., Abbasi, Q., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Internet of Things Journal
• Publisher: IEEE
• ISSN: 2327-4662
• ISSN (Online): 2327-4662
• Published Online: 02 October 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Internet of Things Journal 11(5): 7640 - 7651
• Publisher Policy: Reproduced in accordance with the publisher copyright policy