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Computer Science > Machine Learning

arXiv:2204.12605 (cs)
[Submitted on 26 Apr 2022]

Title:Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning Approach

Authors:Shih-Chun Lin, Chia-Hung Lin, Wei-Chi Chen
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Abstract:Industry 4.0-enabled smart factory is expected to realize the next revolution for manufacturers. Although artificial intelligence (AI) technologies have improved productivity, current use cases belong to small-scale and single-task operations. To unbound the potential of smart factory, this paper develops zero-touch network systems for intelligent manufacturing and facilitates distributed AI applications in both training and inferring stages in a large-scale manner. The open radio access network (O-RAN) architecture is first introduced for the zero-touch platform to enable globally controlling communications and computation infrastructure capability in the field. The designed serverless framework allows intelligent and efficient learning assignments and resource allocations. Hence, requested learning tasks can be assigned to appropriate robots, and the underlying infrastructure can be used to support the learning tasks without expert knowledge. Moreover, due to the proposed network system's flexibility, powerful AI-enabled networking algorithms can be utilized to ensure service-level agreements and superior performances for factory workloads. Finally, three open research directions of backward compatibility, end-to-end enhancements, and cybersecurity are discussed for zero-touch smart factory.
Comments: Submitted for publication in the IEEE Network
Subjects: Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2204.12605 [cs.LG]
  (or arXiv:2204.12605v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2204.12605

Submission history

From: Wei-Chi Chen [view email]
[v1] Tue, 26 Apr 2022 21:41:43 UTC (9,465 KB)
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