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Network challenges of novel sources of Big Data

In his doctoral dissertation, Cand.Sc.(Tech.) Mikhail Komarov investigates novel sources of Big Data and future wireless technologies required to deliver this data to handling points. New data collection algorithms are proposed and the performance of last mile terahertz wireless systems is analysed.

Over the last decade, various technologies have moved to a new, qualitative phase of development. Mobile wireless communications, new data collection, data retrieval and data processing techniques have been introduced, along with a cloud approach to storing data. All this, together with the Internet of Everything paradigm, formed the new concept of Big Data.

Mikhail Komarov’s doctoral dissertation studies two main challenges of future WSNs as sources for Big Data systems: efficient data collection algorithms and the problem of data delivery to Internet access points and, eventually, cloud data storage.

“By studying these problems, I was able to propose new algorithms for data collection in WSNs and analyse the performance of terahertz technology as the last mile multi-gigabit backhauls,” Komarov explains.

The achieved results can be applied for developing new Big Data systems based on novel sources of data.


Public defence of a doctoral dissertation on Monday, 28 November 2016

The doctoral dissertation of Cand.Sc. (Tech.) Mikhail Komarov in the field of wireless communication entitled ‘Network challenges of novel sources of Big Data’ will be publicly examined at the Faculty of Computing and Electrical Engineering of Tampere University of Technology (TUT) in auditorium TB220 in the Tietotalo Building (address: Korkeakoulunkatu 1, Tampere, Finland) at 12 noon on Monday, 28 November 2016. 

The opponent will be Senior Lecturer Dr. Veselin Rakocevic (City University of London, London, UK). Associate Professor Dr. Yevgeni Koucheryavy from the Department of Electronics and Communications Engineering of TUT will act as Chairman.

Further information: Mikhail Komarov,

News submitted by: Tiina Leivo
Keywords: science and research, big data