Age of Information in Multi-source Updating Systems Powered by Energy Harvesting

TL;DR

This paper analyzes the Age of Information in multi-source energy harvesting systems, deriving the distributional properties of AoI using stochastic hybrid systems, and compares different queueing strategies for freshness optimization.

Contribution

It provides the first comprehensive analysis of AoI distribution in EH-powered multi-source systems using MGF and SHS framework, covering various queueing disciplines.

Findings

Closed-form expressions for AoI MGF under multiple queueing strategies

Characterization of AoI distribution in energy harvesting systems

Recovery of existing results as special cases

Abstract

This paper considers a multi-source real-time updating system in which an energy harvesting (EH)-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of Age of Information (AoI). The status updates of each source 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. Unlike most of the existing queueing-theoretic analyses of AoI that focus on characterizing its average when the transmitter has a reliable energy source and is hence not powered by EH (referred henceforth as a non-EH transmitter), our analysis is focused on understanding the distributional properties of AoI in multi-source systems 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 average/MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our results is demonstrated by recovering several existing results as special cases.