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首页> 外文期刊>Ad hoc networks >A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks
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A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks

机译：基于深度加强基于学习的无线充电传感器网络的按需充电算法

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摘要

Wireless rechargeable sensor networks are widely used in many fields. However, the limited battery capacity of sensor nodes hinders its development. With the help of wireless energy transfer technology, employing a mobile charger to charge sensor nodes wirelessly has become a promising technology for prolonging the lifetime of wireless sensor networks. Considering that the energy consumption rate varies significantly among sensors, we need a better way to model the charging demand of each sensor, such that the sensors are able to be charged multiple times in one charging tour. Therefore, time window is used to represent charging demand. In order to allow the mobile charger to respond to these charging demands in time and transfer more energy to the sensors, we introduce a new metric: charging reward. This new metric enables us to measure the quality of sensor charging. And then, we study the problem of how to schedule the mobile charger to replenish the energy supply of sensors, such that the sum of charging rewards collected by mobile charger on its charging tour is maximized. The sum of the collected charging reward is subject to the energy capacity constraint on the mobile charger and the charging time windows of all sensor nodes. We first prove that this problem is NP-hard. Due to the complexity of the problem, then deep reinforcement learning technique is exploited to obtain the moving path for mobile charger. Finally, experimental simulations are conducted to evaluate the performance of the proposed charging algorithm, and the results show that the proposed scheme is very promising.
机译：无线可充电传感器网络广泛用于许多领域。然而，传感器节点的有限电池容量阻碍了其开发。借助无线能量转移技术，采用移动充电器对传感器节点无线充电已成为延长无线传感器网络寿命的有希望的技术。考虑到能量消耗率在传感器之间有显着变化，我们需要更好的方法来建模每个传感器的充电需求，使得传感器能够在一个充电之旅中多次充电。因此，时间窗口用于表示充电需求。为了让移动充电器及时响应这些充电需求并将更多能量转移到传感器，我们引入了新的指标：充电奖励。这项新的指标使我们能够测量传感器充电的质量。然后，我们研究了如何安排移动充电器来补充传感器的能量供应的问题，使得移动充电器收集的充电奖励的总和最大化。收集的充电奖励的总和在移动充电器上的能量容量约束以及所有传感器节点的充电时间窗口。我们首先证明这个问题是NP-HARD。由于问题的复杂性，利用深度加强学习技术来获得移动充电器的移动路径。最后，进行实验模拟以评估所提出的收费算法的性能，结果表明，该方案非常有前途。

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  • 来源
    《Ad hoc networks》 |2021年第1期|102278.1-102278.10|共10页
  • 作者

    Cao Xianbo; Xu Wenzheng; Liu Xuxun; Peng Jian; Liu Tang;

  • 作者单位

    Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China;

    Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China;

    South China Univ Technol Coll Elect & Informat Engn Guangzhou 510641 Peoples R China;

    Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China;

    Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China|Sichuan Normal Univ Coll Comp Sci Chengdu 610068 Sichuan Peoples R China|Sichuan Normal Univ VC & VR Key Lab Chengdu 610068 Sichuan Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wireless rechargeable sensor networks; Time window; Mobile charging; Deep reinforcement learning technique;

    机译：无线可充电传感器网络;时间窗口;移动充电;深增强学习技术;

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