# Online Monitoring of Metric Temporal Logic using Sequential Networks

## TL;DR

This paper introduces a scalable online monitoring framework for Metric Temporal Logic (MTL) using sequential networks, enabling efficient real-time verification of temporal properties in cyber-physical systems.

## Contribution

It presents a novel method to construct sequential networks from MTL specifications, improving efficiency and scalability for online monitoring of discrete and dense time behaviors.

## Key findings

- Demonstrates performance improvements over existing methods.
- Shows scalability in handling complex MTL specifications.
- Validates effectiveness through extensive testing and comparison.

## Abstract

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.