Application of reinforcement learning to wireless sensor networks: models and algorithms
Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communic...
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| Format: | Article |
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Springer
2015
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| Online Access: | http://eprints.sunway.edu.my/302/ |
| _version_ | 1848801795503030272 |
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| author | Yau, Alvin Kok-Lim * Goh, Hock Guan * Chieng, David Kwong, Kae Hsiang |
| author_facet | Yau, Alvin Kok-Lim * Goh, Hock Guan * Chieng, David Kwong, Kae Hsiang |
| author_sort | Yau, Alvin Kok-Lim * |
| building | SU Institutional Repository |
| collection | Online Access |
| description | Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communication, routing and rate control, so that the sensors and sink nodes are able to observe and carry out optimal actions on their respective operating environment for network and application performance enhancements. This article provides an extensive review on the application of RL to WSNs. This covers many components and features of RL, such as state, action and reward. This article presents how most schemes in WSNs have been approached using the traditional and enhanced RL models and algorithms. It also presents performance enhancements brought about by the RL algorithms, and open issues associated with the application of RL in WSNs. This article aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive and applicable to readers outside the specialty of both RL and WSNs. |
| first_indexed | 2025-11-14T21:13:08Z |
| format | Article |
| id | sunway-302 |
| institution | Sunway University |
| institution_category | Local University |
| last_indexed | 2025-11-14T21:13:08Z |
| publishDate | 2015 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | sunway-3022020-10-12T07:39:07Z http://eprints.sunway.edu.my/302/ Application of reinforcement learning to wireless sensor networks: models and algorithms Yau, Alvin Kok-Lim * Goh, Hock Guan * Chieng, David Kwong, Kae Hsiang QA Mathematics QA75 Electronic computers. Computer science Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communication, routing and rate control, so that the sensors and sink nodes are able to observe and carry out optimal actions on their respective operating environment for network and application performance enhancements. This article provides an extensive review on the application of RL to WSNs. This covers many components and features of RL, such as state, action and reward. This article presents how most schemes in WSNs have been approached using the traditional and enhanced RL models and algorithms. It also presents performance enhancements brought about by the RL algorithms, and open issues associated with the application of RL in WSNs. This article aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive and applicable to readers outside the specialty of both RL and WSNs. Springer 2015-11 Article PeerReviewed Yau, Alvin Kok-Lim * and Goh, Hock Guan * and Chieng, David and Kwong, Kae Hsiang (2015) Application of reinforcement learning to wireless sensor networks: models and algorithms. Computing, 97 (11). pp. 1045-1075. ISSN 0010-485X http://link.springer.com/article/10.1007/s00607-014-0438-1 DOI 10.1007/s00607-014-0438-1 |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science Yau, Alvin Kok-Lim * Goh, Hock Guan * Chieng, David Kwong, Kae Hsiang Application of reinforcement learning to wireless sensor networks: models and algorithms |
| title | Application of reinforcement learning to wireless sensor
networks: models and algorithms |
| title_full | Application of reinforcement learning to wireless sensor
networks: models and algorithms |
| title_fullStr | Application of reinforcement learning to wireless sensor
networks: models and algorithms |
| title_full_unstemmed | Application of reinforcement learning to wireless sensor
networks: models and algorithms |
| title_short | Application of reinforcement learning to wireless sensor
networks: models and algorithms |
| title_sort | application of reinforcement learning to wireless sensor
networks: models and algorithms |
| topic | QA Mathematics QA75 Electronic computers. Computer science |
| url | http://eprints.sunway.edu.my/302/ http://eprints.sunway.edu.my/302/ http://eprints.sunway.edu.my/302/ |