Age- and Deviation-of-Information of Signal-agnostic and Signal-aware Sensor Sampling in Networked Monitoring

网络监控中信号不可知和信号感知传感器采样的信息年龄和偏差

• 批准号: 520006080
• 负责人: Professor Dr.-Ing. Markus Fidler
• 金额: --
• 依托单位: Institut für Kommunikationstechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/520006080?language=en
• 关键词: Age; Deviation; Information; Signal; agnostic

We consider a system where a sensor is sampled and the samples are transmitted via a network subject to latency to a remote monitor. Typical examples are in networked control and networked cyber-physical systems in general. An important performance metric of such systems is the age-of-information, that quantifies the freshness of the sensor data at the remote monitor. Generally, the aim is to find a sampling policy that minimizes the age-of-information. In time-triggered systems, samples are taken at certain points in time, thus the sampling is signal-agnostic. Signal-aware sampling applies to event-triggered systems, where a defined sensor event, e.g., if the change of the sensor signal exceeds a threshold, triggers the transmission of a new sample. Event-triggered systems are attractive from a practical point of view, but have been researched to a much lesser extent. Notably, the notion of age-of-information itself is signal-agnostic and hence cannot express the advantages of signal-aware sampling. In this project, we will consider age-related but signal-aware metrics that consider the actual deviation of the information at the remote monitor from the current sensor signal. We will develop signal-aware sampling policies and evaluate their deviation-of-information performance. For this, we will model sensors by random processes and define events with respect to these processes that trigger the transmission of new samples. We will use methods of the deterministic and stochastic network calculus to derive age- and deviation-of-information bounds, that are not exceeded or only exceeded with a small, defined probability. We will include service models of recent network technologies, i.e., IEEE Time Sensitive Networking/IETF Deterministic Networking and 5G Ultra Reliable Low Latency Communications, that are most relevant for the envisioned applications in networked cyber-physical systems. The analytical works will be accompanied by empirical studies in testbed installations. Two exemplary applications that we will consider are networked robotics, where we use our institute's robotic/TSN testbed, and vehicular communications, based on the known SUMO/OMNeT++ simulator coupling.

我们考虑这样一个系统，在该系统中，传感器被采样，并且样本通过网络被传输到远程监控器。典型的例子是网络控制和网络物理系统。这类系统的一个重要性能指标是信息年龄，它量化了远程监视器上传感器数据的新鲜度。一般而言，目标是找到一种最小化信息年龄的抽样策略。在时间触发系统中，采样是在特定时间点进行的，因此采样与信号无关。信号感知采样适用于事件触发系统，其中定义的传感器事件(例如，如果传感器信号的变化超过阈值)会触发新样本的传输。从实用的角度来看，事件触发系统很有吸引力，但研究的程度要少得多。值得注意的是，信息年龄的概念本身是信号不可知的，因此不能表达信号感知采样的优势。在本项目中，我们将考虑与年龄相关但信号感知的指标，这些指标考虑远程监视器上的信息与当前传感器信号的实际偏差。我们将开发信号感知采样策略，并评估其信息偏差性能。为此，我们将通过随机过程对传感器建模，并定义与这些过程相关的事件，这些事件会触发新样本的传输。我们将使用确定性和随机网络演算的方法来推导出年龄和信息偏差的界限，这些界限不会超过或仅以定义的小概率超过。我们将包括最新网络技术的服务模型，即IEEE时间敏感型网络/IETF确定性网络和5G超可靠低延迟通信，它们与联网的网络物理系统中的预期应用最相关。分析工作将伴随着试验台装置的经验研究。我们将考虑的两个示例性应用是网络机器人技术，其中我们使用我们研究所的机器人/TSN试验台，以及基于已知的相扑/OMNeT++模拟器耦合的车辆通信。