The Age of Information in Networks: Moments, Distributions, and Sampling

TL;DR

This paper develops a stochastic hybrid systems approach to analyze the age of information in networks, deriving differential equations for moments and MGFs, and characterizing the distribution of age in line networks with memoryless servers.

Contribution

It introduces a novel SHS-based method to analyze AoI moments and distributions, including explicit results for line networks with preemptive memoryless servers.

Findings

Derived differential equations for AoI moments and MGFs.

Established conditions for convergence of AoI moments and MGFs.

Found that AoI at a node is distributed as a sum of independent exponential or renewal variables.

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.