Data-driven generative AI for sensor fault prediction in distributed networks

In the age of the Internet of Things (IoT), the quantity of devices with the ability to perceive their surroundings is growing continuously. Various services and systems now rely on the data gathered by these devices for making crucial decisions. As a result, the need for accurate data is paramount. However, given the challenging operating conditions, the likelihood of sensor malfunctions is substantial. Consequently, the identification of data irregularities originating from sensors is of great significance.

The objective of this project is to develop a robust smart autonomous system for detecting or possibly predicting faults by monitoring the data obtained from IoT devices within a distributed network while ensuring the integrity, security and privacy. By achieving this objective, we can enhance the reliability and trustworthiness of the data that underpins decision-making processes in IoT-based services and systems.

The following key components will be addressed while working toward the final objective.

1. Data collection and pre-processing
2. Developing machine learning-based fault detection/prediction algorithm
3. Scalability and Adaptability Analysis

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Computer Science, Mathematics, Statistics, Engineering, or related field.

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr. Sana Ullah Jan ([Email Address Removed]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and 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.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• 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.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here.

Download a copy of the project details here.