Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11): 316-321.doi: 10.11896/jsjkx.200400075

• Computer Network • Previous Articles     Next Articles

Optimization of Mobile Charging Path of Wireless Rechargeable Sensor Networks Based on Reinforcement Learning

ZHANG Hao, GUAN Xin-jie, BAI Guang-wei   

  1. Department of Computer Science and Technology,Nanjing University of Technology,Nanjing 211816,China
  • Received:2020-04-17 Revised:2020-07-13 Online:2020-11-15 Published:2020-11-05
  • About author:ZHANG Hao,born in 1995,postgra-duate.Her main research interests include reinforcement learning,artificial intelligence and wireless sensor network.
    GUAN Xin-jie,born in 1984,Ph.D,master instructor.Her main research inte-rests include network optimization,edge computing and software defined network.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61802176).

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Abstract: Wireless sensor networks occupy an important position in environmental perception and target tracking.In order to recharge sensor nodes in time,this paper proposes a low power consumption and high energy efficientcy mobile path charging algorithm based on reinforcement learning.Wireless sensor network uses a mobile charger to charge the sensor nodes.The Q-Lear-ning algorithm and the epsilon-greedy algorithm are combined to complete the charging of all sensor nodes in turn in the shortest path.Existing related researches usually ignore the maximum amount of power that the sensor node itself can withstand,which easily causes the power to exceed the maximum threshold during charging and suspend work,so the charging time of the mobile charger is limited.The result shows that the proposed mobile charging strategy has a higher utility.Compared with the traditional Q-Learning algorithm and the greedy algorithm,the training cycle is greatly reduced and the energy utilization rate is maximized.

Key words: Energy utilization, Mobile charging, Path, Reinforcement learning, Wireless rechargeable sensor network

CLC Number: 

  • TP393
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