🤖 AI Summary
This paper addresses efficiency and scalability bottlenecks in online monitoring of Metric Temporal Logic (MTL) for cyber-physical systems. Methodologically, it introduces a unified monitoring framework supporting both discrete- and dense-time behaviors. Its core contribution is the first “point-free” dense-time modeling approach for MTL, circumventing theoretical limitations inherent in traditional point-based semantics and thereby enhancing monitor composability and engineering practicality. The framework integrates sequence-network-based modeling, syntactic parsing of the past-time fragment of MTL, a unified semantic construction for discrete and dense time, and synergistic optimization via term rewriting and automata-theoretic techniques. Experimental evaluation demonstrates that the prototype monitor outperforms state-of-the-art tools in both runtime speed and memory footprint, achieving real-time deployment capability.
📝 Abstract
Metric Temporal Logic (MTL) is a popular formalism to specify patterns with timing constraints over the behavior of cyber-physical systems. In this paper, I propose sequential networks for online monitoring applications and construct network-based monitors from the past fragment of MTL over discrete and dense time behaviors. This class of monitors is more compositional, extensible, and easily implementable than other monitors based on rewriting and automata. I first explain the sequential network construction over discrete time behaviors and then extend it towards dense time by adopting a point-free approach. The formulation for dense time behaviors and MTL radically differs from the traditional pointy definitions and in return, we avoid some longstanding complications. I argue that the point-free approach is more natural and practical therefore should be preferred for the dense time. Finally, I present my implementation together with some experimental results that show the performance of the network-based monitors compared to similar existing tools.
