Abstract
The evolution of transportation systems has been a cornerstone of human progress, enablingthe movement of people and goods across vast distances with increasing efficiency. In the
modern era, the advent of Intelligent Transportation Systems (ITS) represents a transformative
shift in how we conceive and manage transportation networks. ITS encompasses a wide array
of technologies, applications, and strategies aimed at enhancing the safety, efficiency, and
sustainability of transportation systems.
The increasing complexity of vehicular networks and the rising demand for intelligent
transportation systems necessitate a paradigm shift towards more flexible, efficient, and
secure network architectures. Software-Defined Vehicular Networks (SDVN) have emerged
as a promising solution, leveraging the principles of Software-Defined Networking (SDN)
to enhance the management and security of Vehicular Ad-Hoc Networks (VANETs). This
thesis delves into the challenges and opportunities associated with the integration of SDN
into VANETs, with a particular focus on security, privacy, and communication efficiency.
We propose a comprehensive security framework based on the AES-ECDH algorithm,
designed to ensure secure and authenticated communication between vehicles in an SDNbased
VANET. The framework addresses key security challenges such as data confidentiality,
integrity, and authentication, while also providing a robust mechanism for key exchange and
management. Additionally, we explore the potential of multi-sensor fusion techniques,
combining data from LiDAR, RADAR, and cameras, to enhance object detection and
situational awareness in autonomous vehicles. This approach aims to improve the accuracy
and reliability of perception systems, which are critical for safe and efficient navigation.
This research contributes to the advancement of secure and intelligent transportation
systems by providing a holistic approach to addressing the security and communication challenges
in SDVN. The findings of this study have implications for the design and deployment
of future vehicular networks, paving the way for safer, more efficient, and more reliable
roadways.
| Date of Award | 4 Sept 2024 |
|---|---|
| Original language | English |
| Awarding Institution |
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| Supervisor | Mohammad Abdur Razzaque (Supervisor) & Jie Li (Supervisor) |