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.
Challenges of the Age of Information Paradigm for Metrology in Cyberphysical Ecosystems
Zancanaro A.;Cisotto G.;Badia L.
2022
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.File | Dimensione | Formato | |
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