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Enhancing Fault Detection in Time Sensitive Networks using Machine Learning

Fulltext:


Authors:

Nitin Desai, Sasikumar Punnekkat

Research group:

Dependable Software Engineering

Publication Type:

Conference/Workshop Paper

Venue:

12th International Conference on COMmunication Systems & NETworkS

Abstract

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.

Bibtex

@inproceedings{Desai5775,
author = {Nitin Desai and Sasikumar Punnekkat},
title = {Enhancing Fault Detection in Time Sensitive Networks using Machine Learning},
editor = {IEEE},
month = {January},
year = {2020},
booktitle = {12th International Conference on COMmunication Systems {\&} NETworkS},
url = {http://www.es.mdu.se/publications/5775-}
}