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In this study, we investigate a context-aware status updating systemconsisting of multiple sensor-estimator pairs. A centralized monitor pullsstatus updates from multiple sensors that are monitoring severalsafety-critical situations (e.g., carbon monoxide density in forest firedetection, machine safety in industrial automation, and road safety). Based onthe received sensor updates, multiple estimators determine the currentsafety-critical situations. Due to transmission errors and limitedcommunication resources, the sensor updates may not be timely, resulting in thepossibility of misunderstanding the current situation. In particular, if adangerous situation is misinterpreted as safe, the safety risk is high. In thispaper, we introduce a novel framework that quantifies the penalty due to theunawareness of a potentially dangerous situation. This situation-unawarepenalty function depends on two key factors: the Age of Information (AoI) andthe observed signal value. For optimal estimators, we provide aninformation-theoretic bound of the penalty function that evaluates thefundamental performance limit of the system. To minimize the penalty, we studya pull-based multi-sensor, multi-channel transmission scheduling problem. Ouranalysis reveals that for optimal estimators, it is always beneficial to keepthe channels busy. Due to communication resource constraints, the schedulingproblem can be modelled as a Restless Multi-armed Bandit (RMAB) problem. Byutilizing relaxation and Lagrangian decomposition of the RMAB, we provide alow-complexity scheduling algorithm which is asymptotically optimal. Ourresults hold for both reliable and unreliable channels. Numerical evidenceshows that our scheduling policy can achieve up to 100 times performance gainover periodic updating and up to 10 times over randomized policy.

Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems