Xu, Y.-H., Yu, G. and Yong, Y.-T. (2021) Deep reinforcement learning-based resource scheduling strategy for reliability-oriented wireless body area networks. IEEE Sensors Letters, 5 (1). 7500104. ISSN 2475-1472
Abstract
Reliability is a critical factor in designing of wireless body area networks. In this letter, we propose a resource scheduling strategy and solving an optimization problem to maximize the reliability of the transmission of emergency-critical sensory data. We jointly consider transmission mode, relay selection, time slot allocation, and transmit power of each body sensor and formulating the scheduling problem to be a Markov decision process. In this strategy, the scheduling decision is made by each body sensor that do not have complete and global network information. Owning to the formulated problem is nonconvex and the high computation complexity, we propose a deep reinforcement learning algorithm to solve the problem. Numerical results reveal that the proposed strategy is capacity of guaranteeing the reliability of transmission with an acceptable convergence speed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Sensor networks; deep reinforcement learning; reliable transmission; resource scheduling; wireless body area networks (WBANs) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Feb 2021 16:54 |
Last Modified: | 14 Dec 2021 01:38 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/lsens.2020.3044337 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170978 |