Mobile charging in wireless rechargeable sensor networks is a well-referenced research problem. Numerous studies have been carried out to determine an efficient charging schedule for mobile charger (MC). However, the problem still remains challenging as it requires a wise scheduling decision based on the evaluation of various attributes that impact on network performance. In this regard, multi-attribute decision making (MADM) may be an effective approach which has shown great potential to solve complex decision making problems by coordinating multiple attributes, but has not been explored by existing mobile charging schemes till date. To this end, this paper proposes a novel charging scheme which integrates two popular MADM methods to determine charging schedule by evaluating various network attributes, namely residual energy, distance to MC, energy consumption rate, and neighborhood energy weightage. We take into account both MC’s limited energy and nodes’ uneven energy consumption rates in order to formulate feasibility conditions for scheduling the nodes effectively for further improvement of charging performance. Extensive simulations are performed to illustrate the effectiveness of the proposed scheme. When compared with relevant state-of-the-art methods, the results signify that the proposed scheme boosts charging performance in terms of various performance metrics.

中文翻译：

---

无线可充电传感器网络按需充电调度的多属性决策方法

无线可充电传感器网络中的移动充电是一个很好参考的研究问题。已经进行了大量研究来确定移动充电器 (MC) 的有效充电时间表。然而，该问题仍然具有挑战性，因为它需要基于对影响网络性能的各种属性的评估做出明智的调度决策。在这方面，多属性决策（MADM）可能是一种有效的方法，它通过协调多个属性来解决复杂的决策问题显示出巨大的潜力，但迄今为止，现有的移动计费方案尚未对其进行探索。为此，本文提出了一种新颖的计费方案，该方案集成了两种流行的 MADM 方法，通过评估各种网络属性，即剩余能量、与 MC 的距离、能源消耗率和邻域能源权重。我们同时考虑了MC的有限能量和节点不均匀的能量消耗率，以制定有效调度节点以进一步提高充电性能的可行性条件。进行了广泛的模拟以说明所提出方案的有效性。与相关的最先进方法相比，结果表明所提出的方案在各种性能指标方面提高了充电性能。进行了广泛的模拟以说明所提出方案的有效性。与相关的最先进方法相比，结果表明所提出的方案在各种性能指标方面提高了充电性能。进行了广泛的模拟以说明所提出方案的有效性。与相关的最先进方法相比，结果表明所提出的方案在各种性能指标方面提高了充电性能。