Comparative Analysis of VANET and Vehicular Cloud Models with Advanced Communications Protocols

Abstract

Vehicular communication systems are integral for efficient highway operational management and for mitigating severe traffic congestion. While vehicular ad hoc networks (VANET) are reliable and provide avenues to minimal reliance on existing infrastructure, they can experience high communication overhead and network disruptions. Vehicular micro clouds (VMCs) provide a promising solution to overcome the challenges of VANET by reducing communication latency through cooperative and collaborative resource allocation and data offloading. This thesis offers a comparative performance analysis of freeway incident management and vehicle platooning, comparing VANET communications versus stationary and platoon-based dynamic VMCs. Specifically, it studies speed and lane-changing advisories in addition to freeway platooning. To further enhance the analysis, the performance of both communication architectures is evaluated using communication protocols of DSRC versus cellular technologies of C-V2X, 4G LTE, and 5G NR for latency, bandwidth, range, and deployment considerations. The system-level features, such as driving safety and vehicular mobility are measured to evaluate the efficacy of the communication systems under incident-induced traffic conditions. The study uses the AIMSUN microscopic traffic simulator to model and analyze the performance of the proposed systems. Key performance indicators include communication latency and packet loss ratio. In addition, the stationary and dynamic cloud systems show advantages in reducing travel time delay, even at high penetration rates of the connected vehicles, whilst reducing collision risks. On average, we observe improvements in travel time by 10% by implementing vehicular clouds over traditional ad-hoc networks. From a communications standpoint, the overall latency delay and packet loss are reduced by 7% and 11%, respectively, with the implementation of cloud models. The findings also delineate the benefits of dynamic cloud models, given their improved manoeuvrability, can maximize the computational capabilities of CVs, even at high market penetrations in large-scale freeway demands. The results suggest a shift towards more reliance on connected vehicular clouds to minimize the risks associated with message interference and system overload, whilst fostering advancements in intelligent freeway traffic management systems.