Does Twinning Vehicular Networks Enhance Their Performance in Dense Areas?

TL;DR

This work tackles the challenge of dense-area VANET performance by proposing Digital Twins (DTs) deployed at crowd-prone POIs. It introduces a two-phase approach: first, AI-driven clustering on traffic data identifies crowded POIs (exemplified in Bursa, Turkey), and second, VANET twins are evaluated in edge, cloud, and hybrid configurations using SUMO and OMNeT++ simulations. Key findings show virtual twins substantially reduce latency and accelerate computation compared to physical networks, with cloud-based twins offering the fastest processing and lowest delays at higher vehicle densities. The study provides practical guidance on deploying DT-based VANETs in crowded urban settings and highlights considerations for real-world deployment decisions and future enhancements, including environmental impact analyses and broader POI coverage.

Abstract

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase II, we evaluate the advantages of twinning vehicular networks through three deployment scenarios: edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis demonstrates that twinning significantly reduces network delays, with virtual twins outperforming physical networks. Virtual twins maintain low delays even with increased vehicle density, such as 15.05 seconds for 300 vehicles. Moreover, they exhibit faster computational speeds, with cloud-based twins being 1.7 times faster than edge twins in certain scenarios. These findings provide insights for efficient vehicular communication and underscore the potential of virtual twins in enhancing vehicular networks in crowded areas while emphasizing the importance of considering real-world factors when making deployment decisions.

Figures (8)

• Figure 1: Detailed Description of Presented System Model
• Figure 2: Pre-processing Initial Trajectories
• Figure 3: Post-processing Trajectories
• Figure 4: Clustered Trajectories
• Figure 5: Extracted POIs