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| Task Scheduling Algorithm Based on Q-Learning and Programming for Sensor Nodes |
| WEI Zhenchun1,2, XU Xiangwei 1 , FENG Lin1,2, DING Bei1 |
1.School of Computer and Information, Hefei University of Technology, Hefei 230009
2.Engineering Research Center of Safety Critical Industrial Measurement and Control Technology of Ministry of Education, Hefei University of Technology, Hefei 230009 |
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Abstract To improve the learning policy and obtain better application performance of sensor nodes, a task scheduling algorithm based on Q-learning and programming (QP) for sensor nodes is proposed with the task model of data collection applications. Specifically, some basic learning elements, such as state space, delayed reward and the exploration-exploitation policy, are defined in QP as well. Moreover, according to the characteristics of wireless sensor network(WSN), the programming process based on the expired mechanism and the priority mechanism is established to improve the learning policy by making full use of empirical knowledge. Experimental results show that QP has the ability to perform task scheduling dynamically according to current WSN environments. Compared with other task scheduling algorithms, QP achieves better application performance with reasonable energy consumption.
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Received: 12 April 2016
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| Fund:Supported by National Natural Science Foundation of China (No.61502142,61370088), International Science & Technology Cooperation Program of China (No.2014DFB10060) |
About author:: WEI Zhenchun, born in 1978, Ph.D., associate professor. His research interests include wireless sensor network distributed control and embedded system.
XU Xiangwei, born in 1990, master student. His research interests include wireless sensor network and reinforcement learning.
FENG LinCorresponding author, born in 1979,Ph.D., senior engineer. Her research interests include vehicular ad-Hoc network and wireless network.
DING Bei, born in 1991, master student. His research inte-rests include wireless sensor network and reinforcement lear-ning. |
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2016, Vol. 29 
