Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning
Approach
- URL: http://arxiv.org/abs/2204.12605v1
- Date: Tue, 26 Apr 2022 21:41:43 GMT
- Title: Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning
Approach
- Authors: Shih-Chun Lin, Chia-Hung Lin, and Wei-Chi Chen
- Abstract summary: This paper develops zero-touch network systems for intelligent manufacturing.
It facilitates distributed AI applications in both training and inferring stages in a large-scale manner.
- Score: 14.349058730410109
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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