Modeling Value of Information in Remote Sensing from Correlated Sources

Abstract

This paper investigates remote sensing networks and discusses different models to characterize the Value of Information (VoI), a metric that describes how informative the data transmitted by the sensors are. For each sensor, the VoI evaluations comprise the average node-specific Age of Information (AoI), the average cost spent for sending updates, and the AoI of neighbor nodes, assumed to be correlated sources of information and therefore benefiting the VoI of other sensors nearby. We discuss how this metric can be tracked through a two-dimensional Markov chain, but we also show how this representation can be simplified by including the impact of neighbor nodes within the transition probabilities, so as to obtain a simpler model that gives the same insight in terms of VoI evaluations.

Status Update Scheduling in Remote Sensing Under Variable Activation Delay

“…We consider two cases, i.e., with M =4 and M =5 updates in the time horizon, which are reasonable choices over a short time span for a sensor with limited capabilities [6]. Moreover, we analyze two different distributions for f D (d), namely:…”

“…By merging (6) into the objective of ( 7), we can reformulate the minimizing condition setting the gradient to 0. This actually implies that the first M terms, y 0 , y 1 , .…”

“…It refers to the difference between the present time and the generation instant of the most recently received packet and represents the freshness of the data available at the final endpoint. As such, it is an important metric for evaluating the real-time performance of sensor networks, especially for ambient monitoring, surveillance, and automation in Industry 4.0 or smart living environments, where the timely and accurate detection of events is crucial [5], [6].…”

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

“…The results are reported in Figs. 2 and 3, which show the average AoI of a node versus the number of neighbors n for different values of the transmission probability p [25]. The two figures report different values of the probability q, i.e., a neighbor transmission is useful; specifically, they show q = 0.05 and q = 0.1, respectively.…”