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A secure and flexible edge computing scheme for AI-driven industrial IoT

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Abstract

AI-driven edge computing is a development trend of the Industrial Internet of Things (IIoT). However, most existing solutions ignore the limitations of flexibility, security, and real-time performance caused by the rigid architecture of industrial control systems and the “end-to-end” computing paradigm of IIoT. This paper proposes an edge computing scheme for AI-driven IIoT. Specifically, we design a novel software-defined industry control architecture to enhance the flexibility and security of IIoT edge systems. The architecture decouples the software and hardware of Industrial devices by virtualization and industrial modeling technologies, which improves the flexibility and programmability of IIoT edge systems and alleviates the privacy issue of industrial data. Moreover, we adopt a new edge computing method, dispersed computing, to AI-driven IIoT to achieves better real-time performance and resource utilization. The proposed computing method optimizes the computing and networking of AI-driven industrial applications jointly by a multiobjective optimization scheduling algorithm. We also evaluated the performance of our scheme through experiments.

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Notes

  1. https://github.com/ChanningBJ/NetLatency.

  2. https://github.com/Cloudslab/cloudsim.

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Acknowledgements

This work was supported in National Natural Science Foundation of China (Grant No. 61976064), National Defence Science and Technology Key Laboratory Fund (61421190306).

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  1. Guangzhou, Guangdong, China

    Yan Zhao, Ning Hu & Zhihan Zhu

  2. Chengdu, Sichuan, China

    Yue Zhao

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Zhao, Y., Hu, N., Zhao, Y. et al. A secure and flexible edge computing scheme for AI-driven industrial IoT. Cluster Comput 26, 283–301 (2023). https://doi.org/10.1007/s10586-021-03400-6

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