From Timestamps to Versions: Version AoI in Single- and Multi-Hop Networks

TL;DR

This paper analyzes the distribution of Version Age of Information (VAoI) in single- and multi-hop networks, deriving closed-form expressions and optimizing scheduling policies to improve data freshness and network efficiency.

Contribution

It provides the first comprehensive analysis of VAoI distribution in multi-hop networks, including closed-form solutions and optimal threshold-based scheduling strategies.

Findings

Closed-form expressions for VAoI distribution and average in various policies.

Optimal threshold value for scheduling minimizes VAoI.

Numerical results confirm analytical predictions and insights.

Abstract

Timely and informative data dissemination in communication networks is essential for enhancing system performance and energy efficiency, as it reduces the transmission of outdated or redundant data. Timeliness metrics, such as Age of Information (AoI), effectively quantify data freshness; however, these metrics fail to account for the intrinsic informativeness of the content itself. To address this limitation, content-based metrics have been proposed that combine both timeliness and informativeness. Nevertheless, existing studies have predominantly focused on evaluating average metric values, leaving the complete distribution-particularly in multi-hop network scenarios-largely unexplored. In this paper, we provide a comprehensive analysis of the stationary distribution of the Version Age of Information (VAoI), a content-based metric, under various scheduling policies, including randomized stationary, uniform, and threshold-based policies, with transmission constraints in single-hop and multi-hop networks. We derive closed-form expressions for the stationary distribution and average VAoI under these scheduling approaches. Furthermore, for threshold-based scheduling, we analytically determine the optimal threshold value that minimizes VAoI and derive the corresponding optimal VAoI in closed form. Numerical evaluations verify our analytical findings, providing valuable insights into leveraging VAoI in the design of efficient communication networks.