Li Yue, Yin Zhenyu, Ma Yue, Xu Fulong, Yu Haoyu, Han Guangjie, Bi Yuanguo
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.
Sensors (Basel). 2022 May 30;22(11):4153. doi: 10.3390/s22114153.
Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.
近年来,传统制造工厂一直在加速向智能工厂转型和升级,智能工厂是工业4.0中的一个重要概念。作为工业互联网架构中的关键通信技术,时间敏感网络(TSN)可以突破智能工厂内子系统之间的通信障碍,并为各种网络流形成一个公共网络。传统路由算法不适用于这种新型网络,因为它们会导致不必要的拥塞和延迟。因此,本研究考察了智能工厂中TSN流的分类,将路由问题转化为两个图形问题,并提出了两种启发式优化算法,即GATTRP和AACO,以找到最优解。实验表明,本文提出的算法可以为具有不同时间敏感性的各种TSN流提供更合理的路由安排。这些算法可以分别有效降低高达74%和41%的总体延迟,具有良好的运行性能。