Application of reinforcement learning to wireless sensor networks: models and algorithms

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

Full text not available from this repository.
Official URL: http://link.springer.com/article/10.1007/s00607-01...

Abstract

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.

Item Type: Article
Additional Information: First author is with the Dept. of Computer and Information Systems, Sunway University; 2nd author is with Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahma; 3rd author is with Wireless Communication Cluster, MIMOS Technology Park Malaysia; 4th author is with Recovision R&D,
Uncontrolled Keywords: Wireless sensor networks; reinforcement learning; Q-learning; artificial intelligence; Context awareness
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Sunway University > School of Science and Technology > Dept. Information Systems
Depositing User: Ms. Molly Chuah
Date Deposited: 27 Mar 2016 09:17
Last Modified: 27 Mar 2016 09:17
URI: http://eprints.sunway.edu.my/id/eprint/302

Actions (login required)

View Item View Item