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.
A first degree (at least a 2.1) ideally in Computer Science with a good fundamental knowledge of software engineering or data science, or artificial intelligence or Internet Of Things.
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.
• 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
Some knowledge of machine learning would be beneficial.
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“Context-Active Resilience in Cyber Physical Systems (CAR)”, EU H2020
Marie Skłodowska-Curie Actions – European Fellowships Project, Coordinator,
2016-2018, http://www.msca-car.eu/ .
Claus Pahl, Frank Fowley, Pooyan Jamshidi, Daren Fang and Xiaodong Liu,
“A classification and comparison framework for cloud service brokerage
architectures”, IEEE Transactions on Cloud Computing, accepted, online early
access first, DOI: 10.1109/TCC.2016.2537333, 2016.