Data stream analytics for IoT applications in fog networks

The overall aim of this PhD project is to develop new distributed mechanisms and algorithms for data acquisition and near real-time data stream analytics for IoT applications in fog networks. The application data could be from various IoT scenarios, e.g., energy networks (smart meters, wind turbines, etc.), smart cities (traffic data, environment data), or e-health. In particular, in a large scale IoT network (e.g. wireless sensor network) we are interested in a set of distributed learners that observe data from different sources, which are correlated to a common event that must be classified by the learners. The final classification decision would be based on the combination of the local predictions of all the learners.

This post represents an exciting opportunity for an ambitious graduate to become part of a new cohort of exceptional researchers in a unique partnership between academia and business as part of Keele University’s Smart Energy Network Demonstrator (SEND) project.

Under the supervision of an academic project lead, you will undertake a programme of collaborative research, development and innovation (RD&I) which will underpin the development of a new product, process or service, working collaboratively with a Stoke-on-Trent and Staffordshire business, Assured Systems Ltd. In addition, the postholder will be eligible to submit the work undertaken as the basis for a body of work to be examined for consideration of the award of a PhD from Keele University.

This opportunity is advertised on http://www.keele.ac.uk/vacancies. This is also the link for applications. Applications will only be accepted on the application form on the Keele University vacancies site.
Please ensure you read the full details of the SEND Graduate Researcher vacancy.
SEND Graduate Researcher in Data Stream Analytics for IOT Applications (Job Reference: KU00000675).