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Enhancing Fault Detection in Time Sensitive Networks using Machine Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5269-3900
2020 (English)In: 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 714-719Conference paper (Refereed)
Abstract [en]

Time sensitive networking (TSN) is gaining attention in industrial automation networks since it brings essential real-time capabilities to the Ethernet layer. Safety-critical realtime applications based on TSN require both timeliness as well as fault-tolerance guarantees. The TSN standard 802.1CB introduces seamless redundancy mechanisms for time-sensitive data whereby each data frame is sequenced and duplicated across a redundant link to prevent single points of failure (most commonly, link failures). However, a major shortcoming of 802.1CB is the lack of fault detection mechanisms which can result in unnecessary replications even under good link conditions - clearly inefficient in terms of bandwidth use. This paper proposes a machine learning-based intelligent configuration synthesis mechanism that enhances bandwidth utilization by replicating frames only when a link has a higher propensity for failure. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020. p. 714-719
Keywords [en]
fault-detection, fault-tolerance, machine learning, network configuration, redundancy, safety-critical systems, Time sensitive networking, Bandwidth, Fault tolerance, Learning systems, Safety engineering, Band-width utilization, Fault-detection mechanisms, Industrial automation, Intelligent configuration, Redundancy mechanisms, Safety critical systems, Fault detection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-47457DOI: 10.1109/COMSNETS48256.2020.9027357Scopus ID: 2-s2.0-85082176389ISBN: 9781728131870 (print)OAI: oai:DiVA.org:mdh-47457DiVA, id: diva2:1421161
Conference
2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 7 January 2020 through 11 January 2020
Note

Conference code: 158297; Export Date: 2 April 2020; Conference Paper

Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2020-04-02Bibliographically approved

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Desai, NitinPunnekkat, Sasikumar

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CiteExportLink to record
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Citation style
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