Energy Optimisation Through Path Selection for Underwater Wireless Sensor Networks

Omeke, K. G., Mollel, M. S. , Zhang, L. , Abbasi, Q. H. and Imran, M. A. (2020) Energy Optimisation Through Path Selection for Underwater Wireless Sensor Networks. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194899 (doi: 10.1109/UCET51115.2020.9205429)

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Abstract

This paper explores energy-efficient ways of retrieving data from underwater sensor fields using autonomous underwater vehicles (AUVs). Since AUVs are battery-powered and therefore energy-constrained, their energy consumption is a critical consideration in designing underwater wireless sensor networks. The energy consumed by an AUV depends on the hydrodynamic design, speed, on-board payload and its trajectory. In this paper, we optimise the trajectory taken by the AUV deployed from a floating ship to collect data from every cluster head in an underwater sensor network and return to the ship to offload the data. The trajectory optimisation algorithm models the trajectory selection as a stochastic shortest path problem and uses reinforcement learning to select the minimum cost path, taking into account that banked turns consume more energy than straight movement. We also investigate the impact of AUV speed on its energy consumption. The results show that our algorithm improves AUV energy consumption by up to 50% compared with the Nearest Neighbour algorithm for sparse deployments.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Dr Qammer and Imran, Professor Muhammad and Omeke, Mr Kenechi and Mollel, Michael Samwel and Zhang, Dr Lei
Authors: Omeke, K. G., Mollel, M. S., Zhang, L., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781728194899
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET)
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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