Künzel, Gustavo, Soares Indrusiak, Leandro orcid.org/0000-0002-9938-2920 and Pereira, Carlos Eduardo (2020) Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks:A Q-Learning Approach for Graph Routing. Industrial Informatics, IEEE Transactions on. pp. 5617-5625. ISSN 1551-3203
Abstract
Industrial wireless sensor networks usually have a centralized management approach, where a device known as network manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EPSRC EP/P003664/1 |
Depositing User: | Pure (York) |
Date Deposited: | 30 Apr 2020 15:40 |
Last Modified: | 06 Feb 2025 00:09 |
Published Version: | https://doi.org/10.1109/TII.2019.2941771 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TII.2019.2941771 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160127 |