A unified approach based on semantic models and continuous deep learning to sensor data uncertainty and inconsistency in smart systems

Smart IoT (Internet Of Things) based Applications, such as smart city/building/home and smart factory, are characterized as sensor-driven technology, which has the tendency of producing huge volume of data with increasing velocity. The resulting data produced by these applications are mostly used to support organisation, planning, interpretation and decision-making activities such as context modelling, system adaptation and system evolution. However, these data come with a number of quality issues that collectively results in uncertainties and inconsistencies.

In this project, we aim to innovatively integrate semantics-based data modelling and analysis with continuous deep learning to provide a novel effective solution to the above problem.

The semantic data model will provide a machine-understandable foundation for the IoT data and its analysis, and will be able to produce near real-time solution for the detection and correction of IoT data uncertainties. However, this semantic model may be static and imprecise to cope with the highly dynamic nature of IoT systems and the data they have been generating. Therefore, we propose to use deep learning to support the continuous evolution of the semantic model and its data analysis algorithms.

Academic qualifications

A second class honour degree or equivalent qualification in Computer Science.

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

• Experience of fundamental software design and development
• Competent in design of Internet Of Things applications
• Knowledge of data models and analysis
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management skills

Desirable attributes

•  Some knowledge of machine learning would be beneficial.

Application process

Prospective applicants are encouraged to contact the supervisor, Professor Xiaodong Liu (X.Liunapier.ac.uk) 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.