Funded PhD Opportunity IoTools: Tools to support collection, processing, visualisation and sharing of data from IoT devices.

Subject: Computer Science and Informatics

Summary

The explosive growth in the number of devices connected to the Internet of Things (IoT), coupled with the associated exponential increase in data consumption, demonstrates how the growth of big data analytics perfectly overlaps with that of IoT. The management of this data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security (Davies, 2017,). To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. The convergence of big data, analytics, and IoT, creates several opportunities for flourishing big data and analytics for IoT systems. The value of IoT will come from sharing of information (Nugent, 2016, Synnott 2016). For this to flourish, however, will require the development of automated tools for the capture, processing, validation, visualisation and interpretation of this information (Cleland, 2016).

The aim of this project is to develop automated tools to facilitate the collection, processing, inference, visualisation and sharing of data from IoT devices. The project will leverage aspects of big data and data analytics to widen access to available data in an open science research context. These tools will be applied to application areas within smart cities and connected health.

The objectives of the research are:

1.To investigate and implement intelligent gateway solutions that provide seamless integration and interoperability between various protocols for data collection.

2.To develop enhanced solutions by adding context and meaning to data and helping users process and utilise heterogeneous IoT data at the device, gateway, and cloud levels with various scopes and granularities.

3.To facilitate data sharing through the creation of tools for the intelligent interpretation and visualisation of  IoT data.

4.To engage with stakeholders from multidisciplinary and intersectoral backgrounds including clinical, municipality, industry and research to develop and evaluate these tools.

5.To validate the use of these tools within smart cities and connected health paradigm.

This project will be aligned with the aims of the Connected Health Innovation Centre (CHIC) at Ulster.

Cleland, I., Nugent, C. and Lee, S., 2016, May. The ground truth is out there: challenges with using pervasive technologies for behavior change. In Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 322-325). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

Davies, N. and Clinch, S., 2017. Pervasive Data Science. IEEE Pervasive Computing, 16(3), pp.50-58.

Nugent, C., Cleland, I., Santanna, A., Espinilla, M., Synnott, J., Banos, O., Lundström, J., Hallberg, J. and Calzada, A., 2016, May. An initiative for the creation of open datasets within pervasive healthcare. In Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 318-321). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

Synnott, J., Nugent, C., Zhang, S., Calzada, A., Cleland, I., Espinilla, M., Quero, J.M. and Lundstrom, J., 2016, May. Environment simulation for the promotion of the open data initiative. In Smart Computing (SMARTCOMP), 2016 IEEE International Conference on (pp. 1-6). IEEE.

Essential Criteria

  • Upper Second Class Honours (2:1) Degree from a UK institution (or overseas award deemed equivalent via UK NARIC)

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%

Funding

    Department for the Economy (DfE)

    The scholarships will cover tuition fees at the home rate and, for applicants with UK residence only, a maintenance allowance of £14,777 per annum for three years. EU residents may also apply but, if successful, will receive fees only.

Other information

The Doctoral College at Ulster University

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Key Dates

Submission Deadline
Monday 6 August 2018
Interview Date
mid August 2018

Contact Supervisor

Dr Ian Cleland

Other Supervisors

Apply online

Visit https://www.ulster.ac.uk/applyonline and quote reference number #237846 when applying for this PhD opportunity