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Fog processing in Microgrid-Powered Environments

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  • Full or part time
    Dr T El-Gorashi
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Fog processing is emerging as a complementary paradigm to centeralised cloud processing. It has been developed to address quality of service (QoS) and energy efficiency challenges. Fog processing offloads some of the cloud services into the geographical proximity of users in the network edge to relieve the burden on data centers and networks.

Significant research efforts are focused currently on analysing the essential service metrics with regard to the cost and benefit of offloading services to the fog layer to identify the services that the fog can efficiently host. Such analysis is crucial to sustain the growth of the IoT and Big Data applications facilitated by fog processing which are proving to be pivotal to economic growth and quality of life.

This project will establish for collaboration between the Communication Networks and Systems group and the Smart Energy Systems group to study the potential of leveraging domestic microgeneration, harnessing solar and wind power to improve the viability and sustainability of fog processing solutions. The project will consider a fog processing architecture where fog nodes are small server farms with access to solar and wind power generated in the neighbourhood. It will look at migrating processing services between different fog nodes and the central cloud to treat the uncertainty coming from variation in renewable energy availability and to absorb oversupply, or deal with uncertainty by formalising the problem as a chance-constraint optimisation.

The project will also consider deploying battery storage systems to address the variation in renewable energy availability. The reliability and availability of fog services in urban and rural communities will be optimised based on real-time battery condition monitoring and the optimal charging and discharging control strategies.

Co-supervisors: Professor Jaafar Elmirghani, Professor Kang Li and Dr Petros Aristidou.

Funding Notes

The studentship will partially cover the tuition fees of an international student (excluding EU) for three years by reducing the tuition fees to £5000 per annum. There will be, however, no support for maintenance fees. The successful candidate is also encouraged to take part in up to 250 hours per annum as a lab demonstrator, for which up to an additional £3,000 can be earned.



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