Distributed Intelligence for Self-Organising Cloud–Edge Infrastructures

About the Project

AI-powered cyber-physical and IoT systems are becoming integral to modern environments, with applications in smart cities, Industry 4.0, augmented reality, and autonomous vehicles. These systems increasingly rely on the Cloud–Edge continuum, where heterogeneous and geographically distributed resources must support dynamic, data-intensive, and latency-sensitive workloads. Managing such infrastructures is challenging due to volatility, scalability demands, privacy and security concerns, and the need for context- and location-awareness.

Traditional management approaches often follow a centralised model, where a single entity gathers data and makes global decisions. While effective in cloud-only settings, this model struggles in distributed environments, leading to communication overhead, limited scalability, single points of failure, and privacy risks. In contrast, distributed intelligence provides a more natural fit, enabling systems to self-organise, adapt locally, and collectively maintain global objectives.

This project investigates swarm-inspired self-organisation as a foundation for distributed intelligence in the cloud–edge continuum. The research focuses on characterising the properties required for self-organisation, studying how intelligent agents can dynamically form collectives, and exploring coordination mechanisms that enable adaptive, resilient, and scalable infrastructures.

The key objectives are: 1) To investigate and benchmark decentralised approaches to managing distributed cloud–edge infrastructures; 2) To describe, map, and formalise the problem of self-organisation in distributed intelligence systems; 3) To prototype and evaluate swarm-based strategies to assess their effectiveness for distributed infrastructures.

Academic qualifications

Have, or expect to achieve by the time of start of the studentship a first-class honours degree, or a distinction at master level, ideally in Computer Science or equivalent with a good fundamental knowledge of Computer programming, Software engineering, Cloud computing, Machine learning

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. Full details of the University’s policy are available online.

Essential attributes:

• Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.
• Experience in fundamental software engineering
• Competent in one (or some) programming languages
• Knowledge of Cloud, IoT, Machine learning, and Microservices architecture
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:

• Practical experience in research or industry will be considered an advantage.

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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.Ullah@napier.ac.uk