In this paper, we examine the internet of things system which is dedicated for smart cities, smart factory, and connected cars, etc. To support such systems in wide area with low power consumption, energy harvesting technology without wired charging infrastructure is one of the important issues for longevity of networks. In consideration of the fact that the position and amount of energy charged for each device might be unbalanced according to the distribution of nodes and energy sources, the problem of maximizing the minimum throughput among all nodes becomes a NP-hard challenging issue. To overcome this complexity, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize the node with sufficient energy as the relay. To validate the proposed scheme, we conducted simulations on the system model we established, thus confirm that the proposed scheme is stable and superior to conventional schemes.

翻译：在本文中，我们研究了专门用于智慧城市、智能工厂、连接车辆等的物联网系统。为了支持这样的系统在低功耗下的广域覆盖，没有有线充电基础设施的能量收获技术是网络寿命的重要问题之一。考虑到每个设备的位置和充电能量的数量可能根据节点和能量源的分布而不平衡，最大化所有节点中的最小吞吐量成为 NP-hard 的复杂问题。为了克服这种复杂性，我们提出了一个基于机器学习的中继拓扑算法，具有新颖的反向速率评估方法，以呈现适当的学习方向，并具有一种迭代平衡时间段分配算法，该算法可以利用具有充足能量的节点作为中继。为了验证所提出的方案，我们在我们建立的系统模型上进行了模拟，并确认所提出的方案稳定且优于传统方案。