IEEE 802.1 Time-sensitive Networking (TSN) protocols have recently been proposed to replace legacy networking technologies across different mission-critical systems (MCSs). Design, configuration, and maintenance of TSN within MCSs require advanced methods to tackle the highly complex and interconnected nature of those systems. Accordingly, artificial intelligence (AI) and machine learning (ML) models are the most prominent enablers to develop such methods. However, they usually require a significant amount of data for model training, which is not easily accessible. This short paper aims to recapitulate the need for TSN datasets to flourish research on AI/ML-based techniques for TSN systems. Moreover, it analyzes the main requirements and alternative designs to build a TSN platform to synthesize realistic datasets.
@article{arxiv.2308.10255,
title = {Towards Synthesizing Datasets for IEEE 802.1 Time-sensitive Networking},
author = {Doğanalp Ergenç and Nurefşan Sertbaş Bülbül and Lisa Maile and Anna Arestova and Mathias Fischer},
journal= {arXiv preprint arXiv:2308.10255},
year = {2023}
}