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Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks

Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks

Fatemeh Daneshfar, Vafa Maihami
Copyright: © 2014 |Pages: 32
ISBN13: 9781466644502|ISBN10: 1466644508|EISBN13: 9781466644519
DOI: 10.4018/978-1-4666-4450-2.ch028
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MLA

Daneshfar, Fatemeh, and Vafa Maihami. "Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks." Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, edited by Pandian M. Vasant, IGI Global, 2014, pp. 860-891. https://doi.org/10.4018/978-1-4666-4450-2.ch028

APA

Daneshfar, F. & Maihami, V. (2014). Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks. In P. Vasant (Ed.), Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 860-891). IGI Global. https://doi.org/10.4018/978-1-4666-4450-2.ch028

Chicago

Daneshfar, Fatemeh, and Vafa Maihami. "Distributed Learning Algorithm Applications to the Scheduling of Wireless Sensor Networks." In Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, edited by Pandian M. Vasant, 860-891. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4450-2.ch028

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Abstract

Wireless Sensor Network (WSN) is a network of devices denoted as nodes that can sense the environment and communicate gathered data, through wireless medium to a sink node. It is a wireless network with low power consumption, small size, and reasonable price which has a variety of applications in monitoring and tracking. However, WSN is characterized by constrained energy because its nodes are battery-powered and energy recharging is difficult in most of applications. Also the reduction of energy consumption often introduces additional latency of data delivery. To address this, many scheduling approaches have been proposed. In this paper, the authors discuss the applicability of Reinforcement Learning (RL) towards multiple access design in order to reduce energy consumption and to achieve low latency in WSNs. In this learning strategy, an agent would become knowledgeable in making actions through interacting with the environment. As a result of rewards in response to the actions, the agent asymptotically reaches the optimal policy. This policy maximizes the long-term expected return value of the agent.

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