Towards Synthesizing Datasets for IEEE 802.1 Time-sensitive Networking
This work is incremental, as it focuses on enabling AI/ML research for TSN in mission-critical systems by proposing dataset synthesis methods.
The paper addresses the lack of accessible data for training AI/ML models in IEEE 802.1 Time-sensitive Networking (TSN) systems by analyzing requirements and designs for synthesizing realistic datasets to support research in this area.
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.