Challenges of the Age of Information Paradigm for Metrology in Cyberphysical Ecosystems

Abstract

We are facing a transition towards interconnection of computing systems, people, and things, where boundaries are disappearing and new challenges are emerging. This trend also applies to smart living environments, which are becoming a cyberphysical ecosystem of devices and individuals. Generally, meta-descriptors such as age of information are exploited to obtain efficient content representation and semantic characterization, with the advantage of better data handling. However, the strong relevance of living support in the involved applications imposes to rethink of this approach whenever it is important to factor the human-in-the-loop. In this paper, we discuss how the investigations related to age of information, in particular aimed at statistical descriptions and/or network operation modeling, can be influenced in such scenarios, for what concerns overarching machine learning for data classification and its impact on the sensing frequency, as well as the presence of data correlation that allows for a parsimonious handling of the updates.

Status Update Scheduling in Remote Sensing Under Variable Activation Delay

“…Extensions of the implications found in this scenario to more general semantic communications, e.g., involving retransmissions, feedback, or the structural texture of the content such as video and multimedia [20], [31] can be further explored. Alternatively, future research could focus on developing new techniques for minimizing sensing delay and improving AoI in sensor networks, for example, by means of machine learning techniques to predict the delay terms caused by competing tasks [22], and this feature can be integrated in the scheduling algorithms to further optimize them. Another way to improve the AoI, which is particularly suggested in the context of ultra-reliabile low-latency communications, is through data duplication over different connectivities [32].…”

Impact of Transmission Delays Over Age of Information Under Finite Horizon Scheduling

“…Although at first glance the choice may seem limiting, it can be of high practical relevance as the offline scheduler requires limited computing power which is often a fundamental constraint for battery-powered IoT devices with little computing capabilities. Also, an online scheduler should require constant feedback and this may not be possible in some scenarios [2], [5], [25].…”

“…We derive closed-form analytical expressions that are further evaluated under different system parameters and also verified via simulation. Our analysis enables a better understanding of the impact of transmission delays on AoI in general setups, especially when it affects the system in association with other non-idealities such as erasures [23] or collisions [24], energy unavailability [9], or whenever the statistics of the transmission delay is also unknown and must be preliminarly estimated through learning techniques [25], [26].…”

Age of Information is Not Just a Number: Status Updates Against an Eavesdropping Node

“…Note that considering a nonzero propagation delay results in a shift of the service time. Moreover, the impact of propagation delay has already been analyzed in [6] and is out of scope for this paper. We also adopt a generate at will model, implying that every update, when generated at the transmitter's side, conveys fresh information [7].…”