Digital Twin-Enabled Vehicular Edge Computing

About the Project

Upon the emergence of the Internet of Things (IoT) and Mobile Edge Computing (MEC), the Intelligent Transportation Systems (ITS) has been undergoing a transformative phase with exponential growth in innovative digital services and unprecedented business opportunities. This has been growing in line with the expected dominance of Electric Vehicles (EVs) by the next decade. Projections indicate that the ITS vehicular applications/services market will reach $67.2 Billion by 2028. The future of ITS is envisioned as “information-driven,” characterised by the integration of IoT and edge computing, where EVs are poised to act as both service providers and consumers in this ecosystem. As vehicles transform into IoT devices, they will offload and exchange massive amounts of data, facilitating different innovative on-road commercial services such as real-time traffic management, autonomous driving, wireless charging, and targeted advertising. Nonetheless, the realisation of these applications comes with formidable challenges. Communication's sheer volume, complexity, and diversity necessitate effective data transfer, processing, and security solutions. Besides, the traditional cloud-based approach, relying on data transmission to remote data centres for processing, is ill-suited for ITS applications due to latency and bandwidth constraints.

In response to these challenges, Vehicular Edge Computing (VEC) emerges as a transformative force, a promising branch of the 6G smart wireless communications, reshaping the landscape by bringing computational capabilities, represented by the infrastructure, closer to the source of data generation, i.e., vehicles. While VEC offers significant benefits for ITS, it also introduces its challenges. The inherent resource constraints of VEC nodes and their limited communication coverage range underscore a critical need for efficient resource management and sophisticated data exchange protocols with the moving vehicles. Such a need is exacerbated by the rapid vehicular movement that creates continuous dynamic vehicular topology changes. Moreover, as data processing occurs near vehicles, vulnerability to security breaches and unauthorised access is heightened.

The unreliability of the physical network's Vehicle-to-Everything (V2X) communication and its dynamic topology affect the provision of services in ITS. Thus, this project aims at addressing this issue by developing New potential Digital Twin Network (DTN) solutions as a virtual real-time representation. However, achieving real-time synchronisation between the physical and virtual realms remains problematic, resulting in a temporal gap as the physical network topology evolves faster than its DTN topology. The project aims to:

1. Investigate the multi-scale digital mapping of physical objects and achieve real-time synchronisation between the DTN and the physical system in the multi-service VEC environment.
2. Create a robust DT virtual representation that closely mirrors the current state of the physical ITS VEC network.
3. Devising AI-driven solutions to bridge the observed synchronization gap between the physical VEC network and its DTN due to the dynamic topological changes of the VEC network.
4. Model the problem of adaptive resource allocation in multi-service VEC system, exploring deep learning and evolutionary AI approaches.

Academic qualifications

A first degree (at least a 2.1) in Computing or Engineering with a good fundamental knowledge of research methods.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted.

Essential attributes:

• Experience of fundamental research in vehicular communications, digital twins and cloud/edge computing
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:

• Experience with digital twin and edge computing research
• Experience with simulation environments

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact A.Al-Dubai@napier.ac.uk

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc

Studentship Start Date: October 2026

Funding Notes

International applicants should note that visa application costs and the NHS health surcharge are additional costs to be taken into consideration, and successful applicants will need to cover these expenses themselves.