Li Jishan, Deng Zhichao, Feng Yong, Liu Nianbo
Yunnan Key Laboratory of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500, China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2024 Apr 9;24(8):2386. doi: 10.3390/s24082386.
With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.
随着无线可充电传感器网络（WRSN）的出现，使用移动充电车辆（MCV）对节点进行无线充电已成为现实。然而，现有方法忽略了节点能量补充和移动数据收集过程的有效整合。在本文中，我们提出了一种基于深度强化学习的WRSN联合能量补充和数据收集方案（D-JERDG）。通过利用无人机（UAV）的高机动性，D-JERDG能够持续访问每个集群中的簇头节点，便于数据收集和基于距离的充电。首先，D-JERDG利用K均值算法将网络划分为多个集群，并基于改进的动态路由协议提出了一种簇头选择算法，该算法根据簇成员节点的剩余能量和地理位置选举簇头节点。之后，模拟退火（SA）算法确定最短飞行路径。随后，采用深度强化学习模型多目标深度确定性策略梯度（MODDPG）来控制和优化无人机的瞬时航向和速度，有效规划无人机悬停点。通过重新设计奖励函数，实现了对节点死亡率、无人机吞吐量和平均飞行能耗等多个目标的联合优化。大量仿真结果表明，所提出的D-JERDG实现了多目标联合优化，在吞吐量、时间利用率和充电成本等指标方面相对于基线具有显著优势。
