Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles

2025-08-15

AI Summary

To address the challenge of sustaining low-latency operation of digital twins (DTs) in intelligent transportation systems under constrained computational resources, this paper proposes a distributed DT architecture integrating software-defined vehicular networks and mobile edge computing. The core methodological contribution is a network-aware, scalable collaborative task provisioning algorithm, coupled with a reinforcement learning–driven autonomous agent training mechanism, enabling efficient DT synchronization and elastic scaling in dynamic edge environments. Experimental evaluation is conducted on a realistic autonomous driving traffic simulation. Results demonstrate that the proposed approach reduces DT synchronization error to 5%, increases edge resource utilization to 99.5%, and significantly enhances system robustness, scalability, and resource efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Ensuring uninterrupted Digital Twin operation via resource management
• Integrating Digital Twins with Mobile Edge Computing for low-latency services
• Reducing synchronization errors and optimizing edge resource utilization

Innovation

• Distributed computing architecture integrating DTs and MEC
• Network-aware scalable collaborative task provisioning algorithm
• Reduces synchronization errors to 5% with 99.5% resource utilization