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Abstract
Abstract
An extensive portion of recent research contributions concentrate on Big Data, and propose techniques that assume data availability in abundance. However, Mobile Crowd Sensing (MCS) within the IoT-Ecosystem, should also operate on the assumption that Big Data is not always available. MCS data comes from a diverse variety of participant smartphones at different locations and different times. And as the users are mobile, then MCS solutions should tackle more realistic scenarios, where at the sensing layer the problem of data can be at small data scale, and not only Big Data. To that end, this presentation will focus on small-sample techniques and show that they are an important part for realistic IoT systems.
Prof. Nizar Zorba
Prof. Nizar Zorba
Nizar Zorba received the B.Sc. degree in electrical engineering from JUST University, Jordan, in 2002, the M.Sc. Degree in data communications and the M.B.A. degree from the University of Zaragoza, Zaragoza, Spain, in 2004 and 2005, respectively, and the Ph.D. degree in signal processing for communications from UPC Barcelona, Spain, in 2007.
He is a Full Professor at the Electrical Engineering department at Qatar University, and he is currently the Vice-chair of the IEEE ComSoc Communication Systems Integration and Modeling Technical Committee. He is an IEEE Senior Member. Dr. Zorba is associate/guest editor for the IEEE Communications Letters, IEEE Access, Springer/Eurasip Journal on Wireless Communications and Networking, IEEE Communications Magazine and IEEE Network. He is symposium chair at IEEE ICC 2019, workshop chair at IWCMC 2019 and track chair at IEEE GIIS 2018.
He has more than 110 publications in journals and conference proceedings, and his work led to 5 patents. His research interests span 5G networks optimization, demand-response in smart grids, and crowd management.
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