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A Deep Reinforcement Learning-Based On-Demand Charging Algorithm for Wireless Rechargeable Sensor Networks
الگوریتم شارژ تقاضایی مبتنی بر یادگیری تقویتی عمیق برای شبکه های حسگر قابل شارژ بی سیم-2020 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. Keywords: wireless rechargeable sensor networks | time window | mobile charging | deep reinforcement learning technique |
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