Optimizing Age of Information with Correlated Sources

TL;DR

This paper models and optimizes the Age of Information for correlated sources in wireless networks, proposing policies that are near-optimal and adaptive to unknown, changing correlations, with theoretical and simulation validation.

Contribution

It introduces a simple correlated source model, develops near-optimal scheduling policies, and proposes an online learning heuristic for unknown correlation parameters.

Findings

Max-weight policy outperforms randomized policies in practice.

Policies are within a factor of two of optimal.

AoI improves with network size and correlation understanding.

Abstract

We develop a simple model for the timely monitoring of correlated sources over a wireless network. Using this model, we study how to optimize weighted-sum average Age of Information (AoI) in the presence of correlation. First, we discuss how to find optimal stationary randomized policies and show that they are at-most a factor of two away from optimal policies in general. Then, we develop a Lyapunov drift-based max-weight policy that performs better than randomized policies in practice and show that it is also at-most a factor of two away from optimal. Next, we derive scaling results that show how AoI improves in large networks in the presence of correlation. We also show that for stationary randomized policies, the expression for average AoI is robust to the way in which the correlation structure is modeled. Finally, for the setting where correlation parameters are unknown and time-varying, we develop a heuristic policy that adapts its scheduling decisions by learning the correlation parameters in an online manner. We also provide numerical simulations to support our theoretical results.