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,我们研究了专用于智能城市、智能工厂和相联汽车等的事物互联网系统。为了在电力消耗低的广大地区支持这类系统,没有电线充电基础设施的能源收集技术是网络长寿的重要问题之一。考虑到每个装置的充电位置和能量量可能因节点和能源的分布而不平衡这一事实,在所有节点之间最大限度地增加最低输送量的问题成为NP难以克服的问题。为了克服这一复杂问题,我们提议采用机器学习的中继地形算法,采用新的后空率评估方法,提出适当的学习方向和迭接平衡时间档分配算法,这种算法可以利用节点作为中继器的足够能量。为了验证拟议的计划,我们对我们建立的系统模型进行了模拟,从而确认拟议的计划稳定并优于常规计划。