Age of Information under Source-Aware Truncated ARQ in Multi-Source Wireless Status Updating

2026-04-25

AI Summary

This work addresses the joint optimization of information timeliness and energy consumption in multi-source wireless Internet of Things (IoT) systems by proposing a Source-Aware Truncated ARQ (SATARQ) mechanism. SATARQ introduces, for the first time within truncated ARQ protocols, source-dependent maximum transmission attempts coupled with preemptive update scheduling. By constructing a multidimensional Age of Information (AoI) Markov model, the study derives analytical expressions for the distribution and mean of both per-source AoI and peak AoI. Theoretical analysis and simulations demonstrate that SATARQ effectively uncovers the intrinsic trade-offs among maximum transmission attempts, update generation probabilities, and transmit power, significantly enhancing the system’s timeliness–energy efficiency balance while improving overall energy efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Models for Web evolution

Abstract

This paper studies information timeliness in multi-source wireless Internet of Things (IoT) status updating systems under a truncated Automatic Repeat reQuest (ARQ) protocol. We propose a source-aware truncated ARQ (SATARQ) scheme that allows differentiated maximum transmission times (MTTs) tailored to different sources. This work focuses on a wireless system with preemptive update management. To study the statistical characteristics of the age of information (AoI) process for each source, a multi-dimensional age process (MDAP) is developed and modeled as a Markov chain, tracking both the AoI and the age of the concerned source's update currently in transmission. Via Markov analysis of the MDAP, we obtain analytical expressions for the distributions and averages of the AoI and peak AoI, as well as the average power consumption of IoT device. The timeliness-energy tradeoff is analyzed by examining the impact of the MTT, update generation probability (UGP), and wireless transmission power (TP). Moreover, this work explores the energy efficiency of the wireless status updating process and its relationship with the information timeliness and energy cost. Numerical results validate the theoretical analysis. Finally, it is demonstrated that the proposed SATARQ, combined with the optimization of MTTs, UGPs, and TPs, significantly improves the overall timeliness-energy tradeoff and energy efficiency across all sources.

Problem

• Age of Information
• Multi-Source
• Truncated ARQ
• Timeliness-Energy Tradeoff
• Wireless Status Updating

Innovation

• Source-aware truncated ARQ
• Age of Information
• Multi-dimensional age process
• Timeliness-energy tradeoff
• Markov chain analysis