基于机器学习的自适应双模协同无线充电调度策略

为了打破无线传感器网络的能量瓶颈，考虑无线充电效率对充电距离的敏感性，提出一种基于机器学习的自适应双模式设备协同调度的无线充电方案。首先，基于剩余能量、能耗以及充电效率来定义节点状态，提出一种计及节点状态的自适应阈值选择充电算法。然后设计改进式遗传算法，以最大化能量效用为目标为各节点选择合适的充电模式。此外，为进一步降低充电算法时间复杂度，利用基于支持向量机的机器学习方法学习上述充电模式切换机制，构建节点状态智能预测模型。仿真结果表明，所提算法可在保证较低充电时延的基础上，有效提升多无线充电设备的能量效用，增强传感网络的可持续性。

Adaptive dual-mode cooperative wireless charging scheduling strategy based on machine learning

To break the energy bottleneck of wireless sensor networks and considering the sensitivity of wireless charging efficiency to charging distance, a machine learning-based adaptive dual-mode device co-scheduling scheme is proposed. First, an adaptive threshold selection charging algorithm is proposed to take into account the node states based on residual energy, energy consumption and charging efficiency, and then an improved genetic algorithm is designed to select the appropriate charging mode for each node with the goal of maximizing energy use. In addition, to further reduce the time complexity of the charging algorithm, a support vector machine-based machine learning method is used to learn the above charging mode switching mechanism and construct an intelligent node state prediction model. Simulation results show that the proposed algorithm can effectively improve energy utility of multiple wireless charging devices and enhance the sustainability of the sensing network while ensuring low charging latency.