Age Optimum Sampling in Non-Stationary Environment

TL;DR

This paper introduces an online sampling strategy for status update systems that adaptively minimizes the Age-of-Information in non-stationary environments by detecting changes in delay distribution and learning optimal thresholds.

Contribution

It proposes a joint stochastic approximation and change point detection algorithm for dynamic AoI minimization in non-stationary channels.

Findings

Algorithm quickly detects delay distribution changes.

Proposed policy converges to minimum average AoI.

Effective in non-stationary, unknown delay environments.

Abstract

In this work, we consider a status update system with a sensor and a receiver. The status update information is sampled by the sensor and then forwarded to the receiver through a channel with non-stationary delay distribution. The data freshness at the receiver is quantified by the Age-of-Information (AoI). The goal is to design an online sampling strategy that can minimize the average AoI when the non-stationary delay distribution is unknown. Assuming that channel delay distribution may change over time, to minimize the average AoI, we propose a joint stochastic approximation and non-parametric change point detection algorithm that can: (1) learn the optimum update threshold when the delay distribution remains static; (2) detect the change in transmission delay distribution quickly and then restart the learning process. Simulation results show that the proposed algorithm can quickly detect the delay changes, and the average AoI obtained by the proposed policy converges to the minimum AoI.