计算机科学
信息物理系统
边缘计算
自动化
可靠性(半导体)
软件部署
低延迟(资本市场)
智能电网
能源消耗
GSM演进的增强数据速率
分布式计算
计算机网络
工程类
功率(物理)
电信
机械工程
物理
电气工程
量子力学
操作系统
作者
Yuhuai Peng,Alireza Jolfaei,Qiaozhi Hua,Wen-Long Shang,Keping Yu
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-06-09
卷期号:18 (12): 9292-9301
被引量:8
标识
DOI:10.1109/tii.2022.3181199
摘要
With the rapid development of Industry 4.0, the industrial cyber-physical systems (ICPS) are expected to realize the digital sensing, automatic control, and refined management in smart factories. However, limited bandwidth resources and severe industrial interference make it difficult to meet the real-time and ultrahigh reliability in edge computing (EC)-based next-generation industrial automation networks. To tackle these challenges, in this article, we propose a real-time transmission optimization scheme to accelerate EC. First, we establish a hierarchical system model for smart manufacturing and automation scenarios. Then we present a power control optimization method based on noncooperative game to alleviate interference and reduce energy consumption. Finally, we propose a path optimization scheme based on Q-learning for low-latency and ultrahigh reliability transmission requirements. Extensive simulation results reveal that our proposals perform better in terms of transmission delay and packet-loss rate compared with traditional methods, and therefore, contributes to EC deployment in ICPS.
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