User Behavior Analysis Using IoT Data

  • Azade Fotouhi Telecom & Media Division, Altran Technologies, France
  • Marwa Oudi Telecom & Media Division, Altran Technologies, France
  • Billel Gueni Telecom & Media Division, Altran Technologies, France
  • Mouna Ben Mabrouk Telecom & Media Division, Altran Technologies, France
Keywords: IoT, smart homes, machine learning, smart wireless communication


While the number of connected objects in the world of Internet of Thing (IoT) is increasing, an efficient and intelligent solution to exploit the huge amount of generated data does not exist. Smart homes powered by IoT devises are able to automate and monitor the every day activities of home owners, and improve the life quality especially for elderly and disabled people. In this paper, we propose a machine learning based model in order to analyze the IoT data and to provide data-driven services. Hence, this will make it possible to extract meaningful information from data and make intelligent decisions in smart environments. Then, the proposed model is evaluated using collected data from IoT devices based on different communication protocols.


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Author Biography

Azade Fotouhi, Telecom & Media Division, Altran Technologies, France

Azade Fotouhi received her PhD from University of New South Wales (UNSW), Australia in the school of computer science and engineering. Following a postdoctoral fellowship at Data61|CSIRO, she joined Altran Research and Innovation, France, in 2018. Currently she is working as a Research engineer in the Machine learning and Telecom. She has authored several papers in IEEE journals and conferences, all in recognized venues, and was the recipient of the NASSCOM Student business Innovation Awards, Australia, in 2016. Her research interests include UAV Communication, Machine Learning, IoT, and Mobile Networks.


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