The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The distributions are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, survival rate, and travel distance.

翻译：基于物联网的无线传感器网络面临能量短缺挑战，而新型无线能量传输技术可克服这一难题。无线传感器网络与无线能量传输的结合被称为无线可充电传感器网络，其充电效率与充电调度是核心关注点。为此，本文针对集成感知与通信辅助的无线可充电传感器网络，提出了一种面向多移动充电车辆的按需概率充电调度方案，该方案包含三个部分。首先，综合考虑四个属性及其概率分布以均衡各移动充电车辆的充电负载，这些属性包括：充电节点剩余能量、移动充电车辆到充电节点的距离、充电节点度数以及充电节点介数中心性。其次，采用高效充电因子策略对网络节点进行部分充电。最后，利用集成感知与通信概念高效利用无线资源，以降低各移动充电车辆的行驶成本并避免充电冲突。仿真结果表明，所提协议在能量利用效率、充电延迟、存活率和行驶距离方面均优于现有前沿协议。