Version-Aware Communication in Multi-Hop IoT Networks with Feedback

2026-07-06

AI Summary

This study addresses the lack of timeliness metrics sensitive to the evolution of information content in multi-hop IoT networks and the insufficient characterization of the distributional properties of Version Age of Information (VAoI) under feedback mechanisms. The authors propose a two-layer optimization framework that jointly designs a rate-constrained update policy at the source and a feedback-aware forwarding mechanism at intermediate nodes. For the first time in multi-hop settings, they derive the steady-state VAoI distribution and its analytical relationship with the update rate, leading to a closed-form optimal threshold policy. Both theoretical analysis and simulations demonstrate that the proposed approach significantly reduces communication overhead while effectively preserving data freshness and informational validity at the destination.

Abstract

Timely communication of information in Internet of Things (IoT) networks is critical to enhancing system performance and energy efficiency by minimizing the transmission of outdated or redundant data. Although timeliness metrics such as the Age of Information (AoI) effectively quantify information freshness, they do not account for content evolution. The Version Age of Information (VAoI) addresses this gap by tracking version lag at the receiver, thereby providing a practical content-aware metric. However, prior research has primarily focused on first-moment analyses in single-hop settings, leaving the distributional properties of VAoI in multi-hop networks, as well as the impact of feedback mechanisms, unexplored. In this study, we provide a comprehensive characterization of VAoI in multi-hop networks with transmission constraints and acknowledgment-based feedback. A bi-level optimization framework is formulated to jointly optimize the update policy of a rate-constrained source and the feedback-aware forwarding policies of the intermediate nodes, aiming to minimize communication overhead while maintaining VAoI performance at the destination. We show that the optimal source policy follows a threshold-based update strategy and derive the optimal threshold in closed form. For both the optimal threshold policy and a randomized baseline, we obtain closed-form expressions for the stationary distribution and average VAoI, along with the corresponding update rates across network nodes under feedback-aware forwarding. Numerical results corroborate the analytical findings and illustrate the advantages of utilizing VAoI and feedback to reduce redundant transmissions while preserving data freshness and informativeness in multi-hop systems.

Problem

• Version Age of Information
• multi-hop IoT networks
• feedback mechanisms
• information freshness
• content evolution

Innovation

• Version Age of Information
• multi-hop IoT networks
• feedback-aware forwarding
• threshold-based update policy
• bi-level optimization