Khalid Elgazzar
PhD, P.Eng, SMIEE

• PhD - Computer Science Queen's University, Kingston, Ontario 2013
• MSc - Computer Engineering Arab Academy for Science and Technology, Egypt 2007
• BSc - Computer and Communication Engineering Alexandria University, Egypt 1995

A full list of Dr. Elgazzar's publications can be accessed on Google Scholar.

• Youssef Osman, Reed Dennis and Khalid Elgazzar, “Yield Estimation and Visualization Solution for Precision Agriculture”, Sensors, 2021. 
• Reed Dennis, Youssef Osman, Sifatul Mostafi and Khalid Elgazzar, “Quantitative Analysis of Deep Learning Object Detection Models”, IEEE Access, 2021. 
• Taghreed Alghamdi, Khalid Elgazzar, and Taysseer Sharaf, “Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling” Future Internet, 2021. 
• Ahmed Badr, Abeer Badwai, Khalid Elgazzar, Abdulmonem Rashwan, “Real-time Electrocardiogram Monitoring and Analysis”, ACM Transactions on Computing for Healthcare, 2021. Alaa Khamis, Dipkumar Patel, and Khalid Elgazzar, “Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review”, SI: Deep Learning for Unmanned Systems, Studies in Computational Intelligence, Springer-Verlag, Germany, 2021.
• Ulya Sabeel, Shahram Shah Heydari, Khalid Elgazzar, Khalil El-Khatib, “Training Defensive AI to Detect Atypical Cyberattacks”, IEEE Access, 2021.
• Ghadeer Abdelkader, Khalid Elgazzar, Alaa Khamis, “Connected Vehicles: Technologies Review, State-of-the-Art, Challenges and Opportunities”, Transactions on Intelligent Transportation Systems, 2020.
• Kasem Khalil, Khalid Elgazzar, Mohamed Seliem, Magdy Bayoumi, Resource Discovery Techniques based on Communication Protocols in The Internet of Things: A Survey”, Elsevier Internet of Things Journal, 2020.
• Ashraf Alkhresheh, Khalid Elgazzar, Hossam Hassanein, “DACIoT: Dynamic Access Control Framework for IoT Deployments”, IEEE Internet of Things Journal, 2020. DOI: 10.1109/JIOT.2020.3002709
• Sayani Sarkar, Michael W. Tataro, Khalid Elgazzar, and Ashok Kumar, “Real-Time Object Processing and Routing for Intelligent Drones”, IEEE Computer Journal & Magazine, March 2020.
• Ashraf Alkhresheh, Khalid Elgazzar, Hossam S. Hassanein, “Access Control Models in IoT: Dynamic Authorization Perspective”, Wireless Communications and Mobile Computing, Special Issue on Privacy-preserving IoT Environments, 2019.
• Sittichai Sukreep, Khalid Elgazzar, Cheehung Henry Chu, Chakarida Nukoolkit, and Pornchai Mongkol- nam, “Recognizing Falls, Daily Activities, and Health Monitoring by Smart Devices”, Sensors and Materials, Special Issue on
  Smart Connections and Intelligent Computation in IoT, Vol. 31, No. 6 pp.1847–1869.
• Mohamed Seliem, Khalid Elgazzar, “Towards Privacy Preserving IoT Environments: A Survey”, accepted, Wireless Communications and Mobile Computing, Vol. 2018, Nov. 2018.
• Sittichai Sukreep, Khalid Elgazzar, Henry Chu, Pornchai Mongkolnam, Chakarida Nukoolkit, “iWatch: A Fall and Activity Recognition System Using Smart Devices”, International Journal of Computer and Communication Engineering (IJCCE), Vol. 8, No. 1, January 2018.
• Book Chapter: Operating Secure Mobile Healthcare Services Over Constrained Resource Networks Published in Emergent Computation, January 1, 2017, Anne Kayem, Patrick Martin, Khallid Elgazzar, Christoph Meinel
• Personal Mobile Services Published in Service Oriented Computing and Applications September 17, 2014, Khalid Elgazzar, Patrick MartinHossam, S. Hassanein
  
• Cloud-Assisted Computation Offloading to Support Mobile Services, Published in IEEE Transactions on Cloud Computing, August 22, 2014
• DaaS: Cloud-based Mobile Web Service Discovery, Published in Pervasive and Mobile Computing, August 1, 2014, Khalid Elgazzar, Hossam S.Hassanein & PatrickMartin
  
•  Resilient P2P Architecture for Mobile Resource Sharing, Published in The Computer Journal (Oxford) Volume 58, Issue 8, August 2015, Pages 1689–1700, July 4, 2014, Khalid Elgazzar, Sharief M. A. Oteafy, Walid M. Ibrahim & Hossam S. Hassanei 
• Mobile Web Services: State of the Art and Challenges, Published in International Journal of Advanced Computer Science and Applications, March 1, 2014, Khalid Elgazzar & Patrick Martin
• Appaas: Offering Mobile Applications as a Cloud Service, Published in Journal of Internet Services and Applications, November 4, 2013, Khalid Elgazzar, Ali Ejaz & Hossam S Hassanein

• Accomplishments
• Research Grants

• Design and Analysis for IoT Software Systems (SOFE 4610U)
  Internet of Things (IoT) systems are large scale complex systems integrating sensing, data analytics, software services, and actuation through the Internet. Design and development of software systems for the Internet of Things (IoT) and the related architectures, technologies, communication protocols and frameworks used to enable the IoT are described. The importance and benefits of IoT in society is presented through large scale IoT application examples like smart buildings, remote health monitoring, and smart cities. Students will learn to design and analyze IoT systems with an emphasis on software architecture, protocols at the sensor, actuation, and service layer, and data storage and analysis with the goal of facilitating the deployment of IoT services. IoT specific security concerns and solutions will be discussed. Experiential learning is emphasized through hands‐on design and development of IoT prototypes.
• Introduction to Programming for Engineers (ENGR 1200U)
  Introduction to the anatomy of a computer: CPU, memory, machine cycle, input and output devices, data representation; fundamental programming concepts: flowcharting, algorithm design, use of procedures, program control flow, arrays and vectors, arithmetic and logic operations, input and output, data declaration; programming in C++.
• Real-Time Data Analytics for Internet of Things (ENGR 5785G)
  The velocity, diversity of data types and sheer volumes of data that IoT (Internet of Things) devices collect present unique challenges on data analytics techniques to support real-time decision making. This requires new perspectives in data collection, processing and management flows to cater to such unique requirements. This course focuses on data analytics specific to real-time data streams in IoT environments and applications. The course begins by defining the IoT conceptual framework, outlines application domains and draws clear distinctions between IoT data processing and typical big data analytics. Themes to be covered: IoT data collection architectures, interactions between service models and human subjects, data preprocessing, feature engineering and extraction, statistical modeling and estimation methods, time series analysis, spatiotemporal modeling, multivariate time series forecasting, real-time streaming data pipelines, anomaly detection in real-time sensing, event detection and classification techniques, privacy and security in IoT scenarios, and dynamic access control for real-time IoT.