Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles

TL;DR

This paper presents a distributed edge computing framework integrating Digital Twins and Mobile Edge Computing within vehicular networks, improving robustness and scalability for autonomous vehicle services.

Contribution

It introduces a novel distributed architecture and a network-aware task provisioning algorithm for efficient, scalable Digital Twin management in autonomous vehicle networks.

Findings

Reduces synchronization errors to 5%

Achieves up to 99.5% edge resource utilization

Enhances robustness and scalability of Digital Twins

Abstract

The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through efficient computing resource management remains an open challenge. This paper introduces a distributed computing archi tecture that integrates DTs and Mobile Edge Computing (MEC) within a software-defined vehicular networking framework to enable intelligent, low-latency transportation services. A network aware scalable collaborative task provisioning algorithm is de veloped to train an autonomous agent, which is evaluated using a realistic connected autonomous vehicle (CAV) traffic simulation. The proposed framework significantly enhances the robustness and scalability of DT operations by reducing synchronization errors to as low as 5% while achieving up to 99.5% utilization of edge computing resources.