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

Jul 16, 2026

Upload by Liam

Timely communication in Internet of Things (IoT) networks improves both system performance and energy efficiency by reducing transmission of outdated or redundant data. Age of Information (AOI) measures freshness but ignores content evolution. Version Age of Information (VAOI) fills this gap by tracking version lag at the receiver. This work characterizes VAOI in multi-hop networks with transmission constraints and acknowledgment-based feedback, and proposes a bi-level optimization framework to minimize overhead while preserving destination performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Motivation for semantics-aware communication

Limitations of Age of Information (AOI)

Content-based metrics: AOII and VAOI

Research gap: multi-hop and distributional analysis

Version-Aware Communication in Multi-Hop IoT Networks with Feedback Erfan Delfani and Nikolaos Pappas, Senior Member, IEEE arXiv :2607 .04996v 1 [ cs .NI] 6 Jul 2026 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(IoT) networks is critical to enhancing system performance and energy efficiency by minimizing the transmission of outdated or redundant data.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 firstmoment 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 closedform 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. I. INTRODUCTION Efficient data management is critical for ensuring optimal performance in communication networks across diverse scenarios, ranging from single-hop IoT monitoring systems to multi-hop satellite communication networks. As the volume of data generated by network nodes increases, transmitting all data indiscriminately without considering its semantic significance or task relevance becomes increasingly impractical and unsustainable. Such an approach leads to excessive consumption of critical resources, including energy and bandwidth, ultimately degrading system performance. To address these challenges, there is an urgent need for network management approaches that dynamically optimize data transmission by leveraging semantic metrics to deliver the most timely and informative data within a constrained transmission frequency [2], [3] . This requires a shift from The authors are with the Department of Computer and Information Science at Linkping University, Sweden, email: {erfan.delfani, niko[laos.pappas](laos.pappas}@liu.se. This work)[}](laos.pappas}@liu.se. This work)[@liu.se](laos.pappas}@liu.se. This work)[. This work](laos.pappas}@liu.se. This work) has been supported in part by the Swedish Research Council (VR), ELLIIT, and the European Union (ELIXIRION, 101120135, 6G-LEADER, 101192080, and SOVEREIGN, 101131481) . A shorter version has been published in [1] . traditional passive, semantics-agnostic communication toward active, goal-oriented, semantics-aware approaches. The Age of Information (AOI) [4] is a widely used metric that quantifies the freshness or timeliness of information in status update systems, defined as the time elapsed since the generation of the most recently received data. AoI-aware scheduling minimizes staleness by adapting transmissions to source data arrivals and network service times [5] . However, AOI captures freshness solely through data timestamps, without accounting for actual changes in the source content. As a result, simply refreshing timestamps may fail to deliver new

FAQs

What problem does VAOI solve compared with AOI?

AOI quantifies freshness using timestamps but does not reflect changes in the source content. VAOI measures how many versions the receiver lags behind, making it content-aware.

Why is VAOI considered more practical than AOII?

AOII requires precise knowledge of the information state at both the source and destination, which is often unavailable. VAOI relies on minimal information: the receiver tracks version changes relative to the source.

How does the study optimize communication in multi-hop networks with feedback?

It formulates a bi-level optimization framework that jointly optimizes the source update policy and feedback-aware forwarding policies of intermediate nodes, targeting lower overhead while maintaining VAOI performance at the destination.

Jul 16, 2026

Upload by Liam