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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 1/2013

01.09.2013

Reinforcement Learning for Multiple Access Control in Wireless Sensor Networks: Review, Model, and Open Issues

verfasst von: Mohammad Fathi, Vafa Maihami, Parham Moradi

Erschienen in: Wireless Personal Communications | Ausgabe 1/2013

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Abstract

Wireless sensor networking is a viable communication technology among low-cost and energy-limited sensor nodes deployed in an environment. Due to high operational features, the application area of this technology is extended significantly but with some energy related challenges. One main cause of the nodes energy wasting in these networks is idle listening characterized with no communication activity. This drawback can be mitigated by the means of energy-efficient multiple access control schemes so as to minimize idle listening. In this paper, we discuss the applicability of distributed learning algorithms namely reinforcement learning towards multiple access control (MAC) in wireless sensor networks. We perform a comparative review of relevant work in the literature and then present a cooperative multi agent reinforcement learning framework for MAC design in wireless sensor networks. Accordingly, the paper concludes with some major challenges and open issues of distributed MAC design using reinforcement learning.
Vorheriger Artikel Computationally Efficient Single-Frequency Estimation Scheme in High-Rate WPAN Systems
Nächster Artikel A Low-Complexity Algorithm for Coherent DOA Estimation in Monostatic MIMO Radar

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Metadaten
Titel
Reinforcement Learning for Multiple Access Control in Wireless Sensor Networks: Review, Model, and Open Issues
verfasst von
Mohammad Fathi
Vafa Maihami
Parham Moradi
Publikationsdatum
01.09.2013
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2013
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-013-1028-9

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