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

2026-06-11

AI Summary

This work addresses the challenge of supporting self-triggered traffic in autonomous real-time systems (ARTS), which exhibits high arrival-time variability and frequent absence despite reserved resources, rendering conventional network schedulers ineffective. To overcome these limitations, the paper introduces the first dedicated scheduling paradigm tailored for self-triggered traffic, integrating precise offline time-slot reservation based on Time-Sensitive Networking (TSN) with an adaptive online resource release mechanism. By leveraging inferable arrival information, the proposed approach enhances scheduling accuracy and dynamically reclaims idle time slots to improve resource utilization. Experimental results on both simulation and real-world platforms demonstrate that the method significantly outperforms existing solutions, achieving superior schedulability, scalability, and network efficiency while ensuring reliable transmission.

Abstract

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, we propose ARTSN, an ST-tailored scheduling paradigm built upon time-sensitive networking (TSN). ARTSN introduces two key techniques: (1) an exact offline scheduling method that leverages the inferable arrival information of ST traffic for precise time-slot reservation, and (2) an adaptive online slot-release mechanism that dynamically reclaims unused reservations when ST traffic is absent. 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.

Problem

• self-triggered traffic
• network scheduling
• time-sensitive networking
• resource underutilization
• arrival time uncertainty

Innovation

• Self-triggered traffic
• Time-Sensitive Networking (TSN)
• Exact offline scheduling
• Adaptive slot release
• Real-time autonomous systems