TITLE: Towards Synthesizing Datasets for IEEE 802.1 Time-sensitive Networking 

ABSTRACT: 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.
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.
Towards Synthesizing Datasets for IEEE 802.1 Time-sensitive Networking
http://arxiv.org/abs/2308.10255v1