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.
Similar content being viewed by others
References
Zhou J L, Li L Y, Vajdi A, et al. Temperature-constrained reliability optimization of industrial cyber-physical systems using machine learning and feedback control. IEEE Trans Automat Sci Eng, 2021. doi: https://doi.org/10.1109/TASE.2021.3062408
Zhou X K, Liang W, Shimizu S, et al. Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems. IEEE Trans Ind Inf, 2021, 17: 5790–5798
Li M Y, Guan X P, Hua C Q, et al. Predictive pre-allocation for low-latency uplink access in industrial wireless networks. In: Proceedings of IEEE International Conference on Computer Communications, Honolulu, 2018. 306–314
Li M Y, Chen C L, Hua C Q, et al. A learning-based pre-allocation scheme for low-latency access in industrial wireless networks. IEEE Trans Wireless Commun, 2020, 19: 650–664
Farkas J, Varga B, Mikloòs G, et al. 5G-TSN integration meets networking requirements for industrial automation. Ericsson Technology Review, 2019. https://www.ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/5g-tsn-integration-for-industrial-automation
Fu S, Wu J S, Wen H, et al. Software defined wireline-wireless cross-networks: framework, challenges, and prospects. IEEE Commun Mag, 2018, 56: 145–151
Ke C H, Chen Y S, Yu Y S. Improving video transmission in software defined wired and wireless networks using multi-path transmission. J Commun Netw, 2017, 19: 587–595
Cai L, Shen X S, Mark J W, et al. QoS support in wireless/wired networks using the TCP-friendly AIMD protocol. IEEE Trans Wireless Commun, 2006, 5: 469–480
Underberg L, Kays R, Dietrich S, et al. Towards hybrid wired-wireless networks in industrial applications. In: Proceedings of IEEE Industrial Cyber-Physical Systems (ICPS), Saint Petersburg, 2018. 768–773
Sachs J, Wallstedt K, Alriksson F, et al. Boosting smart manufacturing with 5G wireless connectivity. Ericsson Technology Review, 2019. https://www.ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/boosting-smart-manufacturing-with-5g-wireless-connectivity
Lyons T J. Stochastic finance: an introduction in discrete time. Math Intelligencer, 2004, 26: 67–68
Batewela S, Liu C F, Bennis M, et al. Risk-sensitive task fetching and offloading for vehicular edge computing. IEEE Commun Lett, 2020, 24: 617–621
Bennis M, Debbah M, Poor H V. Ultrareliable and low-latency wireless communication: tail, risk, and scale. Proc IEEE, 2018, 106: 1834–1853
Yang G, Xiao M, Poor H V. Low-latency millimeter-wave communications: traffic dispersion or network densification? IEEE Trans Commun, 2018, 66: 3526–3539
Vu T K, Liu C F, Bennis M, et al. Path selection and rate allocation in self-backhauled mmWave networks. In: Proceedings of IEEE Wireless Communications and Networking Conference, Barcelona, 2018. 1–6
Vu T K, Liu C F, Bennis M, et al. Ultra-reliable and low latency communication in mmWave-enabled massive MIMO networks. IEEE Commun Lett, 2017, 21: 2041–2044
Assaad M, Ahmad A, Tembine H. Risk sensitive resource control approach for delay limited data in wireless networks. In: Proceedings of IEEE Global Telecommunications Conference, Houston, 2011. 1–5
Alsenwi M, Tran N H, Bennis M, et al. eMBB-URLLC resource slicing: a risk-sensitive approach. IEEE Commun Lett, 2019, 23: 740–743
Holfeld B, Wieruch D, Wirth T, et al. Wireless communication for factory automation: an opportunity for LTE and 5G systems. IEEE Commun Mag, 2016, 54: 36–43
3GPP, ETSI. Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN). ETSI TS 136 300 V11.6.0 (2013-07). https://www.etsi.org/deliver/etsi_ts/136300_136399/136300/11.06.00_60/ts_136300v110600p.pdf
Schulz P, Matthe M, Klessig H, et al. Latency critical IoT applications in 5G: perspective on the design of radio interface and network architecture. IEEE Commun Mag, 2017, 55: 70–78
Seo J B, Leung V C M. Performance modeling and stability of semi-persistent scheduling with initial random access in LTE. IEEE Trans Wireless Commun, 2012, 11: 4446–4456
Afrin N, Brown J, Khan J Y. Design of a buffer and channel adaptive LTE semi-persistent scheduler for M2M communications. In: Proceedings of IEEE International Conference on Communications, London, 2015. 5821–5826
Soleymani D M, Puschmann A, Roth-Mandutz E, et al. A hierarchical radio resource management scheme for next generation cellular networks. In: Proceedings of IEEE Wireless Communications and Networking Conference Workshops, Doha, 2016. 416–420
Raza M, Le-minh H, Aslam N, et al. A novel MAC proposal for critical and emergency communications in industrial wireless sensor networks. Comput Electr Eng, 2018, 72: 976–989
Farag H, Sisinni E, Gidlund M, et al. Priority-aware wireless fieldbus protocol for mixed-criticality industrial wireless sensor networks. IEEE Sens J, 2019, 19: 2767–2780
Gaj P, Jasperneite J, Felser M. Computer communication within industrial distributed environment—a survey. IEEE Trans Ind Inf, 2013, 9: 182–189
Zand P, Chatterjea S, Das K, et al. Wireless industrial monitoring and control networks: the journey so far and the road ahead. J Sens Actuator Netw, 2012, 1: 123–152
Lin F L, Dai W B, Li W B, et al. A framework of priority-aware packet transmission scheduling in cluster-based industrial wireless sensor networks. IEEE Trans Ind Inf, 2020, 16: 5596–5606
Hang N T T, Trinh N C, Ban N T, et al. Delay and reliability analysis of p-persistent carrier sense multiple access for multi-event industrial wireless sensor networks. IEEE Sens J, 2020, 20: 12402–12414
Shafiq M Z, Ji L, Liu A X, et al. Large-scale measurement and characterization of cellular machine-to-machine traffic. IEEE/ACM Trans Networking, 2013, 21: 1960–1973
Singh S R, Murthy H A, Gonsalves T A. Feature selection for text classification based on gini coefficient of inequality. In: Proceedings of the Fourth Workshop on Feature Selection in Data Mining, Hyderabad, 2010. 76–85
Arora P, Szepesvári C, Zheng R. Sequential learning for optimal monitoring of multi-channel wireless networks. In: Proceedings of IEEE International Conference on Computer Communications, Shanghai, 2011
Xu Q, Zheng R. When data acquisition meets data analytics: a distributed active learning framework for optimal budgeted mobile crowdsensing. In: Proceedings of IEEE INFOCOM-IEEE Conference on Computer Communications, Atlanta, 2017. 1–9
Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 2018
Acknowledgements
This work was partially supported by National Key Research and Development Program of China (Grant No. 2018YFB1702100) and National Natural Science Foundation of China (Grant Nos. 62025305, 61933009, 62103272).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, Y., Xu, Q., Guan, X. et al. Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols. Sci. China Inf. Sci. 65, 110204 (2022). https://doi.org/10.1007/s11432-020-3344-8
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-020-3344-8