Queen's University

Dr. Hossam Hassanein

Director and Professor, School of Computing
LinkedIn profile for Dr. Hossam Hassanein


I am a Professor and Director of the School of Computing, at Queen's University. I am also the founder and director of the Queen`s Telecommunications Research Lab (TRL) in the School of Computing. Before joining Queen's University, I worked in the department of Mathematics and Computer Science at Kuwait University, and the department of Electrical and Computer Engineering at the University of Waterloo. I earned my PhD in Computing Science from the University of Alberta, my MASc in Computer Engineering from the University of Toronto, and my BSc in Electrical Engineering from Kuwait University. I am a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a former chair of the IEEE Communication Society Ad Hoc and Sensor Networks Technical Committee, and an IEEE Communications Society Distinguished Speaker.

My research is in the control and performance evaluation of broadband, wireless and mobile network architectures and protocols. I am currently leading research in the areas of 5G and edge computing. I have authored, with my students and colleagues, more than 500 publications in journals and conference proceedings. I have received several recognitions and best paper awards at top international conferences. In 2015 I received the Queen’s University School of Graduate Studies Award for Excellence in Graduate Student Supervision an honour that I cherish from my students who are a continual source of great pride for me.


Most Recent Project

Robust Quality Metric for Scarce Mobile Crowd-Sensing Scenarios

Cover page for report by Queen's University researcher Dr. Hossam HassaneinThis conference paper for the 2018 IEEE International Conference on Communications Workshops (ICC), proposes a novel quality of source metric for Mobile Crowd-Sensing systems (MCS), for systems with scarce participant availability due to small sample sizes in each sensing cycle. 

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Other Projects

  • Utilization of Stochastic Modeling for Green Predictive Video Delivery Under Network Uncertainties

    Illustration of wasting resources and QoS degradationsPredictive resource allocation (PRA) has gained momentum in the network research community as a way to cope with the exponential increase in video traffic. Existing PRA schemes have demonstrated profound energy savings and ubiquitous quality of service (QoS) satisfaction under idealistic prediction of future network states. In this paper, we relax the main assumption of existing PRA work and tackle uncertainties in predicted information which resulted from space and time variation of the network load and users demands.

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