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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Bibliographic Details
Published in:Computing 2015-11, Vol.97 (11), p.1045-1075
Main Authors: Yau, Kok-Lim Alvin, Goh, Hock Guan, Chieng, David, Kwong, Kae Hsiang
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Communication
Communications networks
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Decision making
Distance learning
Energy consumption
Humidity
Information Systems Applications (incl.Internet)
Learning
Performance enhancement
Reinforcement
Remote sensors
Sensors
Software Engineering
Studies
Wireless networks
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Summary: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.
ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-014-0438-1