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International Conference on Image Processing and Communications

IP&C 2019: Image Processing and Communications pp 264–271Cite as

Traffic Modeling for Industrial Internet of Things (IIoT) Networks

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1062))

Abstract

This article presents an engineering proposal of methods to model typical traffic sources to be found in industrial networks. To develop these methods, the ON/OFF model was used. On the basis of this particular model, a number of methods are proposed that can be used to model typical types of traffic that occur in industrial networks, such as Time-Triggered (TT) traffic, Audio-Video Bridging (AVB) traffic and Best Effort (BE) traffic. The article presents four traffic models and a method for their application in modeling a number of exemplary types of traffic used in industrial networks.

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References

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Author information

Authors and Affiliations

  1. Faculty of Electronics and Telecommunications, Poznan University of Technology (PUT), Poznań, Poland

    Mariusz Gła̧bowski, Sławomir Hanczewski, Maciej Stasiak, Michał Weissenberg & Piotr Zwierzykowski

  2. Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China

    Vito Bai

Authors
  1. Mariusz Gła̧bowski
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  2. Sławomir Hanczewski
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  3. Maciej Stasiak
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  4. Michał Weissenberg
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  5. Piotr Zwierzykowski
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  6. Vito Bai
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Corresponding author

Correspondence to Michał Weissenberg .

Editor information

Editors and Affiliations

  1. Institute of Telecommunications and Computer Science, University of Science and Technology (UTP), Bydgoszcz, Poland

    Michał Choraś

  2. Department of Telecommunications, Computer Sciences and Electrical Engineering, University of Science and Technology (UTP), Bydgoszcz, Poland

    Ryszard S. Choraś

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Cite this paper

Gła̧bowski, M., Hanczewski, S., Stasiak, M., Weissenberg, M., Zwierzykowski, P., Bai, V. (2020). Traffic Modeling for Industrial Internet of Things (IIoT) Networks. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_31

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