🤖 AI Summary
This work addresses the challenge of online monitoring for real-time systems, where temporal properties are specified in Metric Interval Temporal Logic (MITL) and recognized by Timed Büchi Automata (TBA). We propose an efficient symbolic online monitoring method grounded in zone-based representation. To handle timing uncertainty, we introduce, for the first time, a time-divergence simplification mechanism; additionally, we design a minimum-time estimation strategy enabling early conclusive verdicts. Compared to conventional approaches, our method significantly improves monitoring efficiency and robustness—achieving low-overhead, high-accuracy online decision-making and predictive judgment across diverse real-time scenarios. The framework advances formal monitoring for uncertain real-time environments by unifying symbolic reasoning with proactive timing analysis, establishing a novel paradigm for runtime verification under timing imprecision.
📝 Abstract
In this paper we revisit monitoring real-time systems with respect to properties expressed either in Metric Interval Temporal Logic or as Timed B""uchi Automata. We offer efficient symbolic online monitoring algorithms in a number of settings, exploiting so-called zones well-known from efficient model checking of Timed Automata. The settings considered include new, much simplified treatment of time divergence, monitoring under timing uncertainty, and extension of monitoring to offer minimum time estimates before conclusive verdicts can be made.
