Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks : A Q-Learning Approach for Graph Routing
- Submitting institution
-
University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 66301524
- Type
- D - Journal article
- DOI
-
10.1109/TII.2019.2941771
- Title of journal
- Industrial Informatics, IEEE Transactions on
- Article number
- -
- First page
- 5617
- Volume
- 16
- Issue
- 8
- ISSN
- 1551-3203
- Open access status
- Other exception
- Month of publication
- September
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was one of the main outcomes of Gustavo Künzel’s year-long research visit to the Department of Computer Science in York from September 2018 to August 2019. It presents an approach based on reinforcement learning to dynamically explore the trade-off between performance, reliability and energy-efficiency in industrial networks based on the WirelessHART protocol.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
