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Enhanced time-sensitive networking configuration detection using optimized BPNN with feature selection for industry 4.0

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Abstract

With the advancement of Industry 4.0, Time-Sensitive Networking (TSN) has become essential for ensuring prompt and reliable data transmission. As an augmentation of Ethernet, TSN aims to supply services capable of low latency, minimal jitter, and low packet loss for urgent data in decentralized, user-oriented networks. Efficient detection techniques are integral to TSN for swiftly determining the practicability of network configurations, as existing schedulability analysis proves insufficient. This paper delves into the potential of backpropagation neural networks (BPNN) in schedulability analysis efficiency. We optimize BPNN using spearman correlation feature selection combined with a voting ensemble method and Particle Swarm Optimization (PSO), forming two models: Spearman-Vote-BPNN and Spearman-PSO-BPNN. Testing on 5,000 network configurations in computer simulations, both models demonstrated high generalization accuracy, around 97.4%. Spearman-Vote-BPNN achieved the fastest training speed at 0.63 s and an accuracy of 98.2%. Meanwhile, Spearman-PSO-BPNN showed the highest accuracy (98.5%) with the quickest detection speed (5.6 ms). The outcomes of this research significantly advance the efficacy and precision of TSN network configuration detection and establish a formidable groundwork for future scholarly pursuits in this area.

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Acknowledgements

This work was partly supported by scientific research projects funded by Changzhou University (ZMF22020117, Research on real-time public security service based on software defined wireless sensor networks), scientific research projects funded by Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_3072, TSN Configuration Detection with feature selection and Optimized Machine Learning Algorithm), and cooperative project funded by Jiangsu Wantai Motor Co., Ltd (KYH23020487, Development on standardization system of efficient intelligent manufacturing for motor industry).

Funding

Funding was provided by Changzhou University (Grant No. ZMF22020117), Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX23_3072), and Jiangsu Wantai Motor Co., Ltd (Grant No. KYH23020487).

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Changzhou University, Jiangsu, China

    Cheng Wang, Lin Chen & Yongsong Wang

  2. School of Microelectronics and Control Engineering, Changzhou University, Jiangsu, China

    Chengjie Tang, Yaqiao Xian, Yuhao Zhao & Zhan Huan

  3. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Hai Xue

Authors
  1. Cheng Wang
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  2. Lin Chen
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  3. Chengjie Tang
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  4. Yongsong Wang
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  5. Yaqiao Xian
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  6. Yuhao Zhao
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  7. Hai Xue
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  8. Zhan Huan
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Contributions

All authors made substantive contributions to the conception and design of this research. C.W., L.C. and C.T. proposed the idea of this work and designed the research. C.W. and L.C. designed the study and wrote the manuscript. Y.W. and L.C. executed simulations that are central to our findings and conclusions. C.T., Y.X. and Y.Z. was instrumental in collecting the dataset, which forms the basis of our analysis and discussion. H.X. and Z.H. revisedd the manuscript and offered constructive suggestions. C.W. and L.C. prepared all the figures and tables. All authors reviewed and agreed to the final version of the manuscript.

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Correspondence to Zhan Huan.

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Wang, C., Chen, L., Tang, C. et al. Enhanced time-sensitive networking configuration detection using optimized BPNN with feature selection for industry 4.0. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04493-5

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