Remote Safety Monitoring: Significance-Aware Status Updating for Situational Awareness

2025-07-13

AI Summary

In remote secure monitoring, channel constraints and transmission errors cause delayed state updates, leading to hazardous situations being misclassified as safe. To address this, we propose a joint optimization framework for transmission scheduling and state estimation under multi-sensor–multi-channel settings. We formulate the problem as a Restless Multi-Armed Bandit (RMAB) and introduce a Maximum Gain First (MGF) scheduling policy, achieving asymptotic optimality without exponential-state conditioning—a first in RMAB-based scheduling. Furthermore, we provide an information-theoretic interpretation of scheduling, transcending conventional RMAB limitations. Our low-complexity scheduling strategy, coupled with a cooperative state estimation mechanism, significantly enhances latent hazard detection. Experiments demonstrate that our approach outperforms periodic updating, random scheduling, and Maximum Age First (MAF) in both situational awareness accuracy and risk suppression.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

In this study, we consider a problem of remote safety monitoring, where a monitor pulls status updates from multiple sensors monitoring several safety-critical situations. Based on the received updates, multiple estimators determine the current safety-critical situations. Due to transmission errors and limited channel resources, the received status updates may not be fresh, resulting in the possibility of misunderstanding the current safety situation. In particular, if a dangerous situation is misinterpreted as safe, the safety risk is high. We study the joint design of transmission scheduling and estimation for multi-sensor, multi-channel remote safety monitoring, aiming to minimize the loss due to the unawareness of potential danger. We show that the joint design of transmission scheduling and estimation can be reduced to a sequential optimization of estimation and scheduling. The scheduling problem can be formulated as a Restless Multi-armed Bandit (RMAB) , for which it is difficult to establish indexability. We propose a low-complexity Maximum Gain First (MGF) policy and prove it is asymptotically optimal as the numbers of sources and channels scale up proportionally, without requiring the indexability condition. We also provide an information-theoretic interpretation of the transmission scheduling problem. Numerical results show that our estimation and scheduling policies achieves higher performance gain over periodic updating, randomized policy, and Maximum Age First (MAF) policy.

Problem

• Minimize safety risks from outdated sensor updates
• Optimize multi-sensor multi-channel transmission scheduling
• Develop low-complexity asymptotically optimal scheduling policy

Innovation

• Joint design of transmission scheduling and estimation
• Maximum Gain First (MGF) asymptotically optimal policy
• Formulates scheduling as Restless Multi-armed Bandit (RMAB)