基于强化学习的无线可充电传感网移动充电路径优化

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

无线传感器网络在环境感知、目标跟踪等方面占据了重要地位。为了能够及时地为传感器节点补充能量,提出了一种基于强化学习的低功耗、高能效的移动路径充电算法。无线传感器网络采用移动充电车对传感器节点进行充电,将Q-Learning算法与epsilon-greedy算法相结合,以最短路径依次完成所有传感器节点的充电。现有的相关研究通常忽略了传感器节点自身所能承受电量的最大值,容易导致传感器节点因充电过程中电量超出最大值而暂停工作,因此限制了移动充电车的充电时间。结果表明,所提移动充电策略的效用更高,与传统的Q-Learning算法和贪心算法相比,训练周期大幅度下降且实现了能量利用率最大化。

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.