🤖 AI Summary
Existing studies on version-aware age of information (VAoI) focus solely on its time-average, neglecting the full distribution—a critical limitation for characterizing content freshness and timeliness. This paper bridges that gap by systematically characterizing the steady-state distribution of VAoI in both single-hop and multi-hop networks. Leveraging queueing theory and stochastic processes, we derive closed-form expressions for the steady-state distribution and mean of VAoI under randomized, uniform, and threshold-based scheduling policies. We further propose the first analytically tractable method for optimal threshold design and rigorously prove the optimality of threshold policies in minimizing the VAoI distribution in the stochastic dominance sense. Our framework enables joint, fine-grained modeling of content novelty and timeliness, establishing a novel theoretical foundation and practical design principles for communication network scheduling aimed at optimizing data freshness.
📝 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networks is essential for enhancing system performance and energy efficiency, as it reduces the transmission of outdated or redundant data.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.