ARTSN: Exact and Adaptive Self-triggered Traffic Scheduling for ARTS Networks

Autonomous real-time systems (ARTS), such as self-driving vehicles and robotic assembly lines, are increasingly deployed to improve efficiency, accuracy, and responsiveness with reduced human intervention. In ARTS networks, self-triggered (ST) traffic—initiated by internal decision-making rather than fixed schedules or external events—is becoming prevalent and plays a critical role in enabling timely autonomous actions. However, existing network schedulers do not adequately support ST traffic due to two inherent challenges: volatility, where bounded processing jitter leads to uncertain arrival times, and absence, where reserved network resources remain underutilized when ST traffic does not materialize. To address these challenges, the authors propose ARTSN, an ST-tailored scheduling paradigm built upon time-sensitive networking (TSN).

Extensive experiments on both a TSN simulator and a real-world testbed show that ARTSN significantly improves schedulability, scalability, and efficiency over state-of-the-art methods while maintaining reliable transmission guarantees. The authors report that ARTSN achieves higher schedulability ratios, meaning more ST flows can be accommodated without violating deadlines. Scalability is improved as the number of flows increases, and efficiency is enhanced through reduced resource waste due to the online slot-release mechanism. The experiments compare ARTSN against existing schedulers that do not specifically target ST traffic, demonstrating consistent advantages across various scenarios.