Online Monitoring of Metric Temporal Logic using Sequential Networks

Metric Temporal Logic (MTL) is a popular formalism to specify temporal patterns with timing constraints over the behavior of cyber-physical systems with application areas ranging in property-based testing, robotics, optimization, and learning. This paper focuses on the unified construction of sequential networks from MTL specifications over discrete and dense time behaviors to provide an efficient and scalable online monitoring framework. Our core technique, future temporal marking, utilizes interval-based symbolic representations of future discrete and dense timelines. Building upon this, we develop efficient update and output functions for sequential network nodes for timed temporal operations. Finally, we extensively test and compare our proposed technique with existing approaches and runtime verification tools. Results highlight the performance and scalability advantages of our monitoring approach and sequential networks.