The Age of Information in Networks: Moments, Distributions, and Sampling
Abstract
A source provides status updates to monitors through a network with state defined by a continuous-time finite Markov chain. An age of information (AoI) metric is used to characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach, first order linear differential equations are derived for the temporal evolution of both the moments and the moment generating function (MGF) of the age vector components. It is shown that the existence of a non-negative fixed point for the first moment is sufficient to guarantee convergence of all higher order moments as well as a region of convergence for the stationary MGF vector of the age. The stationary MGF vector is then found for the age on a line network of preemptive memoryless servers. From this MGF, it is found that the age at a node is identical in distribution to the sum of independent exponential service times. This observation is then generalized to linear status sampling networks in which each node receives samples of the update process at each preceding node according to a renewal point process. For each node in the line, the age is shown to be identical in distribution to a sum of independent renewal process age random variables.
Keywords
Cite
@article{arxiv.1806.03487,
title = {The Age of Information in Networks: Moments, Distributions, and Sampling},
author = {Roy D. Yates},
journal= {arXiv preprint arXiv:1806.03487},
year = {2020}
}
Comments
This work was presented in part at the 2018 IEEE Infocom Age of Information Workshop. This version will be (more or less) the same as what will appear in the IEEE Transactions on Information Theory. This work was supported by NSF award 1717041