Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels

2024-12-01

Italian National Conference on Sensors

AI Summary

In vehicular networks (IoV), conventional Age of Information (AoI) modeling suffers from inaccuracy due to dynamic channel conditions and high vehicle mobility, particularly under Doppler-induced channel errors and deterministic service delays. Method: This paper proposes the first joint D/M/1 and M/M/1 queueing model to jointly capture error-prone channel behavior (caused by Doppler shift) and deterministic service latency; it further introduces an environment-aware online data sampling rate adaptation algorithm that relaxes the ideal-channel assumption. Contribution/Results: Theoretical analysis and simulations demonstrate that the proposed hybrid model better reflects real-world vehicular communications, with D/M/1 significantly outperforming M/M/1 in AoI characterization. Under typical high-speed scenarios, the mechanism reduces average system AoI by up to 18.7%, validating the critical role of deterministic service modeling and adaptive transmission rate control in enhancing information freshness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system’s average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles’ data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system’s AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system.

Problem

• Optimizing Age of Information in error-prone IoV channels
• Addressing queue characteristics and vehicle mobility impacts
• Dynamic data extraction rate adjustment for optimal AoI

Innovation

• Dynamic data extraction rate adjustment
• M/M/1 and D/M/1 queuing models
• Online optimization for AoI reduction