Paper Info
Title
Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols
Abstract
The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems (ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service (QoS) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5th generation (5G) wireless communication technology provides ultra-reliable and low-latency communication (URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking (TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network (HTSN) co-designed by 5G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling (SPS) to reduce signaling delay by reserving resources in advance. Then we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing (PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.
Year
DOI
Venue
2022
10.1007/s11432-020-3344-8
SCIENCE CHINA-INFORMATION SCIENCES
Keywords
DocType
Volume
industrial cyber-physical systems (ICPS), 5G-TSN co-design, wireless, wired integrated network, risk-sensitive reinforcement learning, deterministic communication
Journal
65
Issue
ISSN
Citations 
1
1674-733X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yajing Zhang100.68
Qimin Xu204.73
Xinping Guan32791253.38
Cai-Lian Chen483198.98
Mingyan Li500.34