The advent of mobile charging with wireless energy transfer technology has perpetuated the omnipresent wireless rechargeable sensor networks. The existing literature finds that on-demand recharging of the sensor nodes (SNs) can significantly improve the charging performance while using multiple charging vehicles (MCVs) and multi-node charging model. Most of the existing schemes ignore the heterogeneous rate of SNs’ energy consumption, partial charging of the SNs, and joint optimization of multiple network attributes and thus these schemes are deprived from the benefits of prolonging network lifetime to its maximum extent. To this end, this paper addresses all the aforementioned issues and proposes an on-demand multi-node charging scheme for the SNs following a partial charging model. The working of the proposed scheme is twofold. First, charging schedules of the MCVs are generated through optimal halting points by integrating non-dominated sorting genetic algorithm (NSGA-II) and multi-attribute decision making (MADM) approach. Then the charging time at each halting point is decided for the SNs with the help of a partial charging timer. We carry out extensive simulations on the proposed scheme and the results are compared with some existing schemes using various performance metrics. The results confirm the superiority of the proposed scheme over the existing ones.

随着无线能量传输技术的移动充电的出现，使无所不在的无线充电传感器网络永存。现有文献发现，在使用多个充电工具（MCV）和多节点充电模型的同时，传感器节点（SN）的按需充电可以显着提高充电性能。现有的大多数方案都忽略了SN能耗的异质率，SN的部分计费以及对多个网络属性的联合优化，因此这些方案被剥夺了最大限度延长网络寿命的好处。为此，本文解决了所有上述问题，并提出了一种采用部分计费模型的SN随需应变多节点计费方案。拟议方案的工作是双重的。第一，通过将非主导排序遗传算法（NSGA-II）和多属性决策（MADM）方法集成在一起，通过最佳停止点生成MCV的充电时间表。然后借助部分充电计时器为SN确定每个停止点的充电时间。我们对提出的方案进行了广泛的仿真，并使用各种性能指标将结果与一些现有方案进行了比较。结果证实了该方案优于现有方案的优越性。我们对提出的方案进行了广泛的仿真，并使用各种性能指标将结果与一些现有方案进行了比较。结果证实了该方案优于现有方案的优越性。我们对提出的方案进行了广泛的仿真，并使用各种性能指标将结果与一些现有方案进行了比较。结果证实了该方案优于现有方案的优越性。