The Age of Information in Networks: Moments, Distributions, and Sampling
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We examine a source providing status updates to monitors through networks with state given by a continuous-time finite Markov chain. Using an age of information metric, we characterize timeliness by the vector of ages tracked by the monitors. Using a stochastic hybrid systems (SHS) approach, we derive first order linear differential equations for the temporal evolution of both the age moments and a moment generating function (MGF) of the age vector components. We show 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 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 process. For the each node in the line, the age is shown to be identical in distribution to a sum of independent renewal process age random variables.
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