Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems
Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems
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DOI：
10.1109/milcom58377.2023.10356278
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Tasmeen Zaman Ornee;Md Kamran Chowdhury Shisher;Clement Kam;Yin Sun
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文献类型：
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作者：
Tasmeen Zaman Ornee;Md Kamran Chowdhury Shisher;Clement Kam;Yin Sun
在这项研究中，我们研究了一个上下文感知的状态更新系统，由多个传感器估计对。集中式监视器从监视若干安全关键情形（例如，森林火灾探测中的一氧化碳密度、工业自动化中的机器安全以及道路安全）。基于接收到的传感器更新，多个估计器确定当前的安全关键情况。由于传输错误和有限的通信资源，传感器更新可能不及时，导致误解当前情况的可能性。特别是，如果将危险情况误解为安全，则安全风险很高。在本文中，我们介绍了一种新的框架，量化的处罚，由于不知道一个潜在的危险情况。这种情况不知道的惩罚函数取决于两个关键因素：信息年龄（AoI）和观察到的信号值。对于最优估计，我们提供了一个信息理论界的惩罚函数，评估系统的基本性能限制。为了最小化惩罚，我们研究了一个基于拉的多传感器，多信道传输调度问题。我们的分析表明，对于最优估计，保持信道忙碌总是有益的。由于通信资源的限制，调度问题可以建模为一个不安分的多臂强盗（RMAB）问题。通过利用松弛和拉格朗日分解的RMAB，我们提供了一个低复杂度的调度算法是渐近最优的。我们的结果适用于可靠和不可靠的渠道。数值实验表明，我们的调度策略可以实现高达100倍的性能增益超过定期更新和随机策略的10倍。
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.