School of Information Science and Engineering, Central South University, Changsha 410083, China.
Sensors (Basel). 2018 May 17;18(5):1601. doi: 10.3390/s18051601.
With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency.
随着边缘计算的出现,大量的设备(如传感器节点)已经被部署在边缘网络中,用于感知和处理数据。然而,如何为这些边缘设备提供实时按需能源是边缘网络的一个新的挑战问题。在边缘计算的实际应用中,由于环境的影响,传感器节点通常具有不同的任务负担,这导致不同节点的能耗率发生动态变化。因此,传统的周期性充电模式不能满足具有动态能耗的节点的充电需求。在本文中,我们提出了一种在移动充电器容量有限的情况下的实时按需充电调度方案(RCSS)。在构建充电路径的过程中,RCSS 充分考虑了不同节点的动态能耗,并提出了下一个节点选择算法。同时,还提出了一种确定充电电路可行性的方法,以确保充电效率。在充电过程中,RCSS 基于自适应充电阈值来降低节点死亡率。与现有方法相比,所提出的 RCS 在生存节点数量、平均服务时间和充电效率方面都取得了更好的性能。