Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles

Fuente: arXiv
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Auteurs principaux: Mallik, Anik, Chen, Dawei, Han, Kyungtae, Xie, Jiang, Han, Zhu
Format: Preprint
Publié: 2024
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author Mallik, Anik
Chen, Dawei
Han, Kyungtae
Xie, Jiang
Han, Zhu
author_facet Mallik, Anik
Chen, Dawei
Han, Kyungtae
Xie, Jiang
Han, Zhu
contents Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility of vehicles. Our research shows that a CAV's relative distance and speed play an essential role in determining the Age-of-Information (AoI). With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs. In this paper, we propose a novel AoI-based service aggregation method for CAVs, which can process the information updates according to their update cycles. First, the AoI for sensors and vehicles is modeled, and a predictive AoI system is designed. Then, to reduce the overall service aggregation time and computational load, intervals are used for periodic AoI prediction, and information sources are clustered based on the AoI value. Finally, the system aggregates services for CAV applications using the predicted AoI. We evaluate the system performance based on data sequencing success rate (DSSR) and overall system latency. Lastly, we compare the performance of our proposed system with three other state-of-the-art methods. The evaluation and comparison results show that our proposed predictive AoI-based service aggregation system maintains satisfactory latency and DSSR for CAV applications and outperforms other existing methods.
format Preprint
id arxiv_https___arxiv_org_abs_2403_08931
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles
Mallik, Anik
Chen, Dawei
Han, Kyungtae
Xie, Jiang
Han, Zhu
Systems and Control
Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility of vehicles. Our research shows that a CAV's relative distance and speed play an essential role in determining the Age-of-Information (AoI). With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs. In this paper, we propose a novel AoI-based service aggregation method for CAVs, which can process the information updates according to their update cycles. First, the AoI for sensors and vehicles is modeled, and a predictive AoI system is designed. Then, to reduce the overall service aggregation time and computational load, intervals are used for periodic AoI prediction, and information sources are clustered based on the AoI value. Finally, the system aggregates services for CAV applications using the predicted AoI. We evaluate the system performance based on data sequencing success rate (DSSR) and overall system latency. Lastly, we compare the performance of our proposed system with three other state-of-the-art methods. The evaluation and comparison results show that our proposed predictive AoI-based service aggregation system maintains satisfactory latency and DSSR for CAV applications and outperforms other existing methods.
title Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles
topic Systems and Control
url https://arxiv.org/abs/2403.08931