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

TL;DR

This work treats AoI as a lever to enhance service aggregation in connected and autonomous vehicles (CAVs) by introducing a predictive AoI-based framework. It models source-specific AoI, compares multiple predictors, and selects an LSTM-based $N$-step-ahead AoI predictor within a periodic prediction scheme governed by the speed-to-coverage-area ratio $SCAR$. A five-step service-aggregation pipeline uses predicted AoI to decide when to initiate, maintain, or terminate information-update connections and clusters sources to reduce compute load. Evaluations in OMNeT++ show the approach sustains sub-threshold latency and high data sequencing success rate (DSSR) across speeds, outperforming FIFO, Stop-N-Wait, and Priority Queue baselines. The results suggest predictive AoI can substantially improve low-latency CAV applications by enabling timely, ordered updates with reduced computational burden.

Abstract

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.

Figures (8)

• Figure 1: Mean AoI satisfaction rate of stationary sensors and moving vehicles at different relative speeds of the ego vehicle.
• Figure 2: Service aggregation problem for varying mobility of the ego vehicle in a CAV scenario.
• Figure 3: Comparison of AoI prediction latency, memory consumption, and accuracy of different prediction models.
• Figure 4: AoI prediction latency, periodic AoI prediction latency, and periodic AoI prediction accuracy using LSTM network.
• Figure 5: (a) Mean system latency at different relative speed and (b) overall latency of system tasks.