Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things

Zihui Luo; Xiaolong Zheng; Bo Wang; Qifeng Meng; Helei Cui; Xiaobing Guo; Liang Liu

doi:10.1109/ICPADS56603.2022.00016

Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things

Zihui Luo, Xiaolong Zheng, Bo Wang, Qifeng Meng, Helei Cui, Xiaobing Guo, Liang Liu

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

Original language	English
Title of host publication	Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022
Publisher	IEEE Computer Society
Pages	57-64
Number of pages	8
ISBN (Electronic)	9781665473156
DOIs	https://doi.org/10.1109/ICPADS56603.2022.00016
State	Published - 2023
Event	28th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2022 - Nanjing, China Duration: 10 Jan 2023 → 12 Jan 2023

Publication series

Name	Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume	2023-January
ISSN (Print)	1521-9097

Conference

Conference	28th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2022
Country/Territory	China
City	Nanjing
Period	10/01/23 → 12/01/23

Keywords

Computing Network Collaboration
Edge Computing
Fog Computing
IIoT
Time-Sensitive Networking

Access to Document

10.1109/ICPADS56603.2022.00016

Cite this

Luo, Z., Zheng, X., Wang, B., Meng, Q., Cui, H., Guo, X., & Liu, L. (2023). Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things. In Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022 (pp. 57-64). (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS; Vol. 2023-January). IEEE Computer Society. https://doi.org/10.1109/ICPADS56603.2022.00016

Luo, Zihui ; Zheng, Xiaolong ; Wang, Bo et al. / Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things. Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022. IEEE Computer Society, 2023. pp. 57-64 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS).

@inproceedings{5bf8ab0b9f89472e917f05e3eb69c489,

title = "Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things",

abstract = "The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.",

keywords = "Computing Network Collaboration, Edge Computing, Fog Computing, IIoT, Time-Sensitive Networking",

author = "Zihui Luo and Xiaolong Zheng and Bo Wang and Qifeng Meng and Helei Cui and Xiaobing Guo and Liang Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 28th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2022 ; Conference date: 10-01-2023 Through 12-01-2023",

year = "2023",

doi = "10.1109/ICPADS56603.2022.00016",

language = "英语",

series = "Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS",

publisher = "IEEE Computer Society",

pages = "57--64",

booktitle = "Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022",

}

Luo, Z, Zheng, X, Wang, B, Meng, Q, Cui, H, Guo, X & Liu, L 2023, Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things. in Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022. Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS, vol. 2023-January, IEEE Computer Society, pp. 57-64, 28th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2022, Nanjing, China, 10/01/23. https://doi.org/10.1109/ICPADS56603.2022.00016

Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things. / Luo, Zihui; Zheng, Xiaolong; Wang, Bo et al.
Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022. IEEE Computer Society, 2023. p. 57-64 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS; Vol. 2023-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things

AU - Luo, Zihui

AU - Zheng, Xiaolong

AU - Wang, Bo

AU - Meng, Qifeng

AU - Cui, Helei

AU - Guo, Xiaobing

AU - Liu, Liang

PY - 2023

Y1 - 2023

N2 - The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

AB - The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

KW - Computing Network Collaboration

KW - Edge Computing

KW - Fog Computing

KW - IIoT

KW - Time-Sensitive Networking

UR - http://www.scopus.com/inward/record.url?scp=85152904284&partnerID=8YFLogxK

U2 - 10.1109/ICPADS56603.2022.00016

DO - 10.1109/ICPADS56603.2022.00016

M3 - 会议稿件

AN - SCOPUS:85152904284

T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS

SP - 57

EP - 64

BT - Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022

PB - IEEE Computer Society

T2 - 28th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2022

Y2 - 10 January 2023 through 12 January 2023

ER -

Luo Z, Zheng X, Wang B, Meng Q, Cui H, Guo X et al. Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things. In Proceedings - 2022 IEEE 28th International Conference on Parallel and Distributed Systems, ICPADS 2022. IEEE Computer Society. 2023. p. 57-64. (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS). doi: 10.1109/ICPADS56603.2022.00016

Hierarchical Computing Network Collaboration Architecture for Industrial Internet of Things

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this