Closed-form Characterization of the MGF of AoI in Energy Harvesting Status Update Systems

TL;DR

This paper derives closed-form expressions for the moment generating function of Age of Information in energy harvesting status update systems, providing insights into its distribution and moments under various queueing disciplines.

Contribution

It introduces a novel analysis of AoI distribution via MGF in energy harvesting systems using stochastic hybrid systems, extending existing results to energy-constrained scenarios.

Findings

Closed-form MGF expressions for AoI under multiple queueing disciplines.

First and second moments of AoI derived for energy harvesting systems.

Numerical results highlight the importance of higher moments in system optimization.

Abstract

This paper considers a real-time status update system in which an energy harvesting (EH)-powered transmitter node observes some physical process, and sends its sensed measurements in the form of status updates to a destination node. The status update and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. We quantify the freshness of status updates when they reach the destination using the concept of Age of Information (AoI). Unlike most of the existing analyses of AoI focusing on the evaluation of its average value when the transmitter is not subject to energy constraints, our analysis is focused on understanding the distributional properties of AoI through the characterization of its moment generating function (MGF). In particular, we use the stochastic hybrid systems (SHS) framework to derive closed-form expressions of the MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and preemptive in service/waiting strategies. Using these MGF results, we further obtain closed-form expressions for the first and second moments of AoI in each queueing discipline. We demonstrate the generality of this analysis by recovering several existing results for the corresponding system with no energy constraints as special cases of the new results. Our numerical results verify the analytical findings, and demonstrate the necessity of incorporating the higher moments of AoI in the implementation/optimization of real-time status update systems rather than just relying on its average value.