Cloud control for IIoT in a cloud-edge environment

doi:10.23919/JSEE.2024.000074

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (4): 1013-1027.doi: 10.23919/JSEE.2024.000074

• CONTROL THEORY AND APPLICATION • Previous Articles

Cloud control for IIoT in a cloud-edge environment

Ce YAN¹(), Yuanqing XIA¹^,*(), Hongjiu YANG²(), Yufeng ZHAN¹()

¹ School of Automation, Beijing Institute of Technology, Beijing 100081, China
² School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Received:2022-03-01 Online:2024-08-18 Published:2024-08-06
Contact: Yuanqing XIA E-mail:yancemc@163.com;xia_yuanqing@bit.edu.cn;yanghongjiu@tju.edu.cn;yu-feng.zhan@bit.edu.cn
About author:
YAN Ce was born in 1991. He received his B.S. degree in mathematics and applied mathematics from Hebei University of Science and Technology, Shijiazhuang, China, in 2014, and M.E. degree in control theory and control engineering from Yanshan University, Qinhuangdao, China, in 2017. He is currently working toward his Ph.D. degree in control science and engineering in Beijing Institute of Technology, Beijing, China. His research interests include cloud control systems and application, cloud workflow scheduling, industrial IoT and intelligent manufacturing systems. E-mail: yancemc@163.com

XIA Yuanqing was born in 1971. He received his M.S. degree in fundamental mathematics from Anhui University, China, in 1998 and Ph.D. degree in control theory and control engineering from Beihang University, Beijing, China, in 2001. He is now the dean of School of Automation, Beijing Institute of Technology. His research interests are cloud control systems, networked control systems, robust control and signal processing, active disturbance rejection control, and unmanned system control. E-mail: xia_yuanqing@bit.edu.cn

YANG Hongjiu was born in 1981. He received his B.S. degree in mathematics and applied mathematics and M.S. degree in applied mathematics from Hebei University of Science and Technology, Shijiazhuang, China, in 2005 and 2008, respectively. He received his Ph.D. degree in control science and engineering from Beijing Institute of Technology, Beijing, China. He was an associate professor with the Department of Automation, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China, in 2013 and 2018. He is currently a professor with the Department of Automation, School of Electrical and Information Engineering, Tianjin University, China. His main research interests include robust control/filter theory, delta operator systems, networked control systems, and active disturbance rejection control. E-mail: yanghongjiu@tju.edu.cn

ZHAN Yufeng was born in 1990. He received his Ph.D. degree in control science and engineering from Beijing Institute of Technology in 2018. He is an assistant professor with the School of Automation, Beijing Institute of Technology, Beijing, China. His research interests include networking systems, game theory, and machine learning. E-mail: yu-feng.zhan@bit.edu.cn

Abstract

Abstract:

The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.

Key words: 5G and time sensitive network (TSN), industrial Internet of Things (IIoT) workflow, transmission control protocol (TCP) flows control, cloud edge collaboration, multi-objective optimal scheduling

Ce YAN, Yuanqing XIA, Hongjiu YANG, Yufeng ZHAN. Cloud control for IIoT in a cloud-edge environment[J]. Journal of Systems Engineering and Electronics, 2024, 35(4): 1013-1027.

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$\alpha $	Fitness value	$\alpha $	Fitness value
0.1	129.53	0.6	119.53
0.2	128.2	0.7	123.14
0.3	126.98	0.8	108.11
0.4	121.18	0.9	115.12
0.5	119.26

Cloud control for IIoT in a cloud-edge environment

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