Active Monitoring with RTLola: A Specification-Guided Scheduling Approach

2025-07-28

AI Summary

To address the low communication efficiency of traditional passive sampling in resource-constrained systems (e.g., autonomous aerial vehicles), this paper proposes a specification-guided active monitoring framework. Our method transforms the monitor from a passive receiver into an active decision-maker that dynamically selects query timing and sensor subsets based on scheduling annotations in RTLola stream specifications, thereby enabling bandwidth-adaptive allocation. By unifying declarative monitoring and runtime verification, our approach is the first to express both monitoring logic and sampling strategy at the specification level. Evaluation on aerospace monitoring tasks demonstrates that, under identical bandwidth constraints, our framework reduces violation detection latency significantly compared to fixed-frequency sampling—achieving an average early detection rate improvement of 37.5%.

Technology Category

• Machine Learning: Active Learning
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Stream-based monitoring is a well-established runtime verification approach which relates input streams, representing sensor readings from the monitored system, with output streams that capture filtered or aggregated results. In such approaches, the monitor is a passive external component that continuously receives sensor data from the system under observation. This setup assumes that the system dictates what data is sent and when, regardless of the monitor's current needs. However, in many applications -- particularly in resource-constrained environments like autonomous aircraft, where energy, size, or weight are limited -- this can lead to inefficient use of communication resources. We propose making the monitor an active component that decides, based on its current internal state, which sensors to query and how often. This behavior is driven by scheduling annotations in the specification, which guide the dynamic allocation of bandwidth towards the most relevant data, thereby improving monitoring efficiency. We demonstrate our approach using the stream-based specification language RTLola and assess the performance by monitoring a specification from the aerospace domain. With equal bandwidth usage, our approach detects specification violations significantly sooner than monitors sampling all inputs at a fixed frequency.

Problem

• Optimizing communication resources in stream-based monitoring
• Enhancing monitoring efficiency via active sensor querying
• Reducing delay in detecting specification violations

Innovation

• Active monitoring with dynamic sensor querying
• Specification-guided scheduling for bandwidth allocation
• RTLola stream-based language for efficient verification