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

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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.