The FORA Fog Computing Platform for Industrial IoTShow others and affiliations
2021 (English)In: Information Systems, ISSN 0306-4379, E-ISSN 1873-6076, Vol. 98, article id 101727Article in journal (Refereed) Published
Abstract [en]
Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT), which use different computation and communication technologies. Cloud Computing cannot be used for OT involving industrial applications, since it cannot guarantee stringent non-functional requirements, e.g., dependability, trustworthiness and timeliness. Instead, a new computing paradigm, called Fog Computing, is envisioned as an architectural means to realize the IT/OT convergence. In this paper we propose a Fog Computing Platform (FCP) reference architecture targeting Industrial IoT applications. The FCP is based on: deterministic virtualization that reduces the effort required for safety and security assurance; middleware for supporting both critical control and dynamic Fog applications; deterministic networking and interoperability, using open standards such as IEEE 802.1 Time-Sensitive Networking (TSN) and OPC Unified Architecture (OPC UA); mechanisms for resource management and orchestration; and services for security, fault tolerance and distributed machine learning. We propose a methodology for the definition and the evaluation of the reference architecture. We use the Architecture Analysis Design Language (AADL) to model the FCP reference architecture, and a set of industrial use cases to evaluate its suitability for the Industrial IoT area.
Place, publisher, year, edition, pages
Elsevier Ltd , 2021. Vol. 98, article id 101727
Keywords [en]
AADL, Deterministic virtualization, Fog Computing, Industrial IoT, Industry 4.0, Time-Sensitive Networking, Computer architecture, Fault tolerance, Fog, IEEE Standards, Industrial internet of things (IIoT), Interoperability, Middleware, Network security, Process control, Architecture analysis, Communication technologies, Distributed machine learning, Non-functional requirements, Opc unified architectures, Reference architecture, Resource management, Safety and securities
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-53523DOI: 10.1016/j.is.2021.101727ISI: 000638134900007Scopus ID: 2-s2.0-85100531828OAI: oai:DiVA.org:mdh-53523DiVA, id: diva2:1541631
2021-04-012021-04-012021-06-15Bibliographically approved