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

In this study, we investigate a context-aware status updating system consisting of multiple sensor-estimator pairs. A centralized monitor pulls status updates from multiple sensors that are monitoring several safety-critical situations (e.g., carbon monoxide density in forest fire detection, machine safety in industrial automation, and road safety). Based on the received sensor updates, multiple estimators determine the current safety-critical situations. Due to transmission errors and limited communication resources, the sensor updates may not be timely, resulting in the possibility of misunderstanding the current situation. In particular, if a dangerous situation is misinterpreted as safe, the safety risk is high. In this paper, we introduce a novel framework that quantifies the penalty due to the unawareness of a potentially dangerous situation. This situation-unaware penalty function depends on two key factors: the Age of Information (AoI) and the observed signal value. For optimal estimators, we provide an information-theoretic bound of the penalty function that evaluates the fundamental performance limit of the system. To minimize the penalty, we study a pull-based multi-sensor, multi-channel transmission scheduling problem. Our analysis reveals that for optimal estimators, it is always beneficial to keep the channels busy. Due to communication resource constraints, the scheduling problem can be modelled as a Restless Multi-armed Bandit (RMAB) problem. By utilizing relaxation and Lagrangian decomposition of the RMAB, we provide a low-complexity scheduling algorithm which is asymptotically optimal. Our results hold for both reliable and unreliable channels. Numerical evidence shows that our scheduling policy can achieve up to 100 times performance gain over periodic updating and up to 10 times over randomized policy.Comment: 7 pages, 4 figures, part of this manuscript has been accepted by IEEE MILCOM 2023 Workshop on QuAVo

Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems

This paper investigates goal-oriented communication for remote estimation of multiple Markov sources in resource-constrained networks. An agent selects the update order of the sources and transmits the packet to a remote destination over an unreliable delay channel. The destination is tasked with source reconstruction for the purpose of actuation. We utilize the metric cost of actuation error (CAE) to capture the significance (semantics) of error at the point of actuation. We aim to find an optimal sampling policy that minimizes the time-averaged CAE subject to average resource constraints. We formulate this problem as an average-cost constrained Markov Decision Process (CMDP) and transform it into an unconstrained MDP by utilizing Lyapunov drift techniques. Then, we propose a low-complexity drift-plus-penalty(DPP) policy for systems with known source/channel statistics and a Lyapunov optimization-based deep reinforcement learning (LO-DRL) policy for unknown environments. Our policies achieve near-optimal performance in CAE minimization and significantly reduce the number of uninformative transmissions

Importance-Aware Fresh Delivery of Versions over Energy Harvesting MACs

We consider a scenario where multiple users, powered by energy harvesting, send version updates over a fading multiple access channel (MAC) to an access point (AP). Version updates having random importance weights arrive at a user according to an exogenous arrival process, and a new version renders all previous versions obsolete. As energy harvesting imposes a time-varying peak power constraint, it is not possible to deliver all the bits of a version instantaneously. Accordingly, the AP chooses the objective of minimizing a finite-horizon time average expectation of the product of importance weight and a convex increasing function of the number of remaining bits of a version to be transmitted at each time instant. The objective enables importance-aware delivery of as many bits, as soon as possible. In this setup, the AP optimizes the objective function subject to an achievable rate-region constraint of the MAC and energy constraints at the users, by deciding the transmit power and the number of bits to be transmitted by each user. We obtain a Markov Decision Process (MDP)-based optimal online policy to the problem and derive structural properties of the policy. We then develop a neural network (NN)-based online heuristic policy, for which we train an NN on the optimal offline policy derived for different sample paths of energy, version arrival and channel power gain processes. Via numerical simulations, we observe that the NN-based online policy performs competitively with respect to the MDP-based online policy

Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

— Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%