Monitoring Spatially Distributed Cyber-Physical Systems with Alternating Finite Automata

2025-03-27

AI Summary

Monitoring spatiotemporal safety properties in spatially distributed networked cyber-physical systems (e.g., mobile UAV swarms) is challenging due to dynamically evolving component connectivity induced by spatial reconfiguration, leading to difficulties in modeling, low monitoring efficiency, and poor scalability. To address this, we propose a novel monitoring framework based on Spatio-Temporal Reach-and-Escape Logic (STREL). Our approach introduces the first semantic encoding of STREL into weighted alternating finite automata (AFA), enabling compact modeling of spatiotemporal behaviors over dynamic weighted graphs. We further design a unified architecture integrating offline preprocessing with online real-time verification. Evaluation on UAV swarm simulations demonstrates that our method significantly improves monitoring efficiency and scalability—supporting millisecond-level runtime verification of complex safety properties for large-scale systems. This work establishes a verifiable and deployable paradigm for ensuring safety in CPS with time-varying topologies.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Modern cyber-physical systems (CPS) can consist of various networked components and agents interacting and communicating with each other. In the context of spatially distributed CPS, these connections can be dynamically dependent on the spatial configuration of the various components and agents. In these settings, robust monitoring of the distributed components is vital to ensuring complex behaviors are achieved, and safety properties are maintained. To this end, we look at defining the automaton semantics for the Spatio-Temporal Reach and Escape Logic (STREL), a formal logic designed to express and monitor spatio-temporal requirements over mobile, spatially distributed CPS. Specifically, STREL reasons about spatio-temporal behavior over dynamic weighted graphs. While STREL is endowed with well defined qualitative and quantitative semantics, in this paper, we propose a novel construction of (weighted) alternating finite automata from STREL specifications that efficiently encodes these semantics. Moreover, we demonstrate how this automaton semantics can be used to perform both, offline and online monitoring for STREL specifications using a simulated drone swarm environment.

Problem

• Monitoring distributed CPS with dynamic spatial dependencies
• Defining automaton semantics for Spatio-Temporal Reach and Escape Logic
• Efficient offline and online monitoring using alternating finite automata

Innovation

• Alternating finite automata for STREL semantics
• Dynamic weighted graphs for spatio-temporal reasoning
• Offline and online monitoring in drone swarms