Thursday, 08 October 2020, 12:00 noon – 1:00 pm EDT (Ottawa time)
Big Data-Driven AI-based Wireless Networks Personalization
Dr. Rawan Alkurd
Data Scientist, Larus Technologies, Ottawa, Canada
PhD in Electrical and Computer Engineering, Carleton University
There is a consensus that trends in emerging wireless technologies, such as explosive data requirements and proliferating services and applications, are creating serious issues for the management of user experience. This is due to the fact that, unlike Quality-of-Service (QoS), for which quantitative metrics are available such as packet error rate, it is difficult to analytically model and measure “user experience”. Besides, the current communication networks use design methodologies that prevent the realization of maximum network efficiency. Therefore, more agile, intelligent, and flexible networks are required. Such networks should be capable of micro-managing resources in a way that meets each user’s service quality expectations while using a minimum amount of resources. This micro-management of network resources has ushered in the concept of wireless network personalization.
In this talk, the concept of wireless network personalization will be presented. The utilization of Artificial Intelligence (AI), big data analytics, and real-time non-intrusive user feedback to enable the personalization of wireless networks will be discussed. Finally, since user privacy is a crucial concern, the utilization of Differential Privacy to preserve users’ privacy in personalized wireless networks will be addressed.
Bio: Rawan Alkurd received her Ph.D. degree in Electrical and Computer Engineering from the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada, in 2020, and her M.Sc. and B.Sc. degrees in Electrical Engineering from Khalifa University, UAE, in 2015 and 2013, respectively. In 2016, she received the Vanier Canada Graduate Scholarship. She is currently working as a Data Scientist at Larus Technologies, Ottawa, Canada. Her research interests include big data, artificial intelligence, machine learning, and their applications in various fields including wireless networks.