2019 article
Provisioned Data Distribution for Intelligent Manufacturing via Fog Computing
47TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 47), Vol. 34, pp. 893–902.
author keywords: Fog Computing; MTConnect Industry 4.0; IIoT; Industrial Cyber-Physical Systems; Big Data; Digital Manufacturing; Data Distribution
TL;DR:
This paper proposes a decentralized data distribution architecture to democratize the potential availability of large amounts of data generated by the manufacturing industry using the Fog Computing paradigm, and demonstrates a data-centric approach which allows peer-to-peer data sharing laterally within the fog layer to serve cloud users.
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open access via doi.org (publisher)
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Added: September 7, 2020
The number of ‘things’ ranging from simple devices to complex machines on the factory floor connected at the enterprise level and to the broader internet is growing exponentially. This connection also leads to a tremendous amount of data generated leading to ‘Data’ now considered one of the core assets in the broader manufacturing industry. However, the availability of this asset is hardly made use of by Small and Medium scale manufacturing enterprises (SME) - the ‘Mittelstand’ of America. How can certain types of data be shared by SME companies, yet have the ability to retain ownership and control over their own data? How does SME leverage computing on these diverse forms of data for the benefit of its clients and itself? In this paper, we propose a decentralized data distribution architecture to democratize the potential availability of large amounts of data generated by the manufacturing industry using the Fog Computing paradigm. The architecture leverages an Industry scalable middleware extension of Cloud manufacturing that securely filters and transmits data from IoT enabled manufacturing machines on the shop floor to potential users over the cloud. This work also demonstrates a data-centric approach which allows peer-to-peer data sharing laterally within the fog layer to serve cloud users. We demonstrate the feasibility of the Fog middleware infrastructure through case studies that involves various types of manufacturing data.