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：本文研究了专为智慧城市、智能工厂及网联汽车等场景设计的物联网系统。为在广域范围内以低功耗支持此类系统，无需有线充电基础设施的能量采集技术成为保障网络长期运行的关键问题之一。考虑到各设备充电位置和能量分布可能因节点与能源分布不均而失衡，最大化所有节点间最小吞吐量的优化问题成为具有NP难特性的挑战性课题。为克服这一复杂性，我们提出了一种基于机器学习的中继拓扑算法，该算法采用新颖的反向传递速率评估方法提供正确的学习方向，并设计了迭代式时隙均衡分配算法，能够将能量充足的节点用作中继。为验证所提方案，我们在所建立的系统模型上进行了仿真，结果表明该方案具有稳定性且优于传统方案。