Dr. Rabeb Mizouni
Dr. rabeb mizouni Electrical Engineering And Computer Science

Contact Information
rabeb.mizouni@ku.ac.ae +971 2 312 4146


Dr. Rabeb Mizouni received her M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Concordia University, Montreal, Canada, in 2007 and 2002, respectively. She is currently an Associate Professor of the Department of Electrical Engineering and Computer Science at Khalifa University. Her current research interests include Blockchain, Machine Lerning, Internet of Things (IoT),  Crowdsensing/Crowdsouring, and Cloud Computing.

  • Ph.D., Department of Electrical and Computer Engineering, Concordia University (Canada), 2007
  • M.Sc., Department of Electrical and Computer Engineering, Concordia University (Canada), 2002.

  • Human Computer Interaction (ECCE636)
  • Introduction to Human Computer Interfaces (HSEG601)
  • iOS App Development (BUSS456)
  • Object-Oriented Programming (ECCE230)
  • Software Architecture (ECCE438)
  • Software Engineering (ECCE438)
  • Software Testing and Quality Assurance (COSC602)

Affiliated Research Institutes/Centers
  • Center for Cyber-Physical Systems

Research Interests
  • Crowdsensing/Crowdsourcing; IoT; AI; Blockchain; Mobile computing; Service Computing

Research Projects

Trust and privacy in Mobile crowdsensing and IoT

C2PS (https://www.ku.ac.ae/c2ps) 

Recently, with the notable advances in crowd sensing and mobile phone sensing, an intelligent combination between CPS based and crowd sensed data has become appealing. The mobility of the users and the high computational capabilities of their devices, combined with the network infrastructure, open the door to more advanced applications. In these settings, we envision a “multi-source” data collection. Such combination of information is suitable for feeding multi-view AI models and is expected to allow faster, more intelligent, and more efficient control of the environment. While appealing, the integration of crowdsourced and sensed data within CPSs is challenging. Due to heterogeneity, the combination requires new boosting techniques to end-up with a trustworthy result. This problem spans several dimensions, including distinguishing between sensor faultiness and maliciousness, detecting bias in crowd sensing system, and integrating both.

Detecting, Localizing, and Tracking Radiation Sources using IoT and Crowd-based Sensing

This project aims to develop a robust, efficient, and effective nuclear radiation monitoring system.  This system should be   capable of providing continuous high precision and real-time detection, localization and tracking of small mobile and immobile radiation sources and unexpected radioactive materials, within a noisy radioactive environment in an urban setting. The proposed monitoring system utilizes Artificial Intelligence (AI), Internet of Things (IoT) and Mobile Crowd Sensing (MCS). 

A Secure and Resilient Chat/VoIP Application over Private Mesh Networks

TII (https://www.tii.ae/)

Mesh Chat applications emerged to extend the connectivity of users and enable the exchange of messages through phone mesh networks. These applications demand security and resiliency in the application and network layers to provide users with the opted for service. In this proposal, we envision joint design and implementation between KU C2PS and TII of a secure chat application for Android phones over a resilient phone mesh network. The proposal aims to incorporate different technologies and techniques such mesh ad-hoc connections and routing protocols, blockchain, machine learning to provide security, resiliency, and sustainability.

Crowdsourcing V2V Platform for Supply Chain Management Over Blockchain

This project proposes a novel Blockchain-based supply chain framework that utilizes crowdsourcing and vehicle-to-vehicle (V2V) technologies to optimize the multistage supply chain process by efficiently managing the resources among involved businesses and crowdsourced participants. The platform is in line with Supply Chain 4.0, providing better flexibility in terms of management through ad-hoc and real-time planning. The deployment of Blockchain provides a trusted infrastructure that establishes trust among businesses and ensures secure payment. V2V technology introduces another layer of communication among selected participants to improve the quality of service in the supply chain. Crowdsourcing technologies will be used to efficiently delegate tasks, increase agility, reduce cost, and improve last-mile delivery logistics. The primary outcome of the project is to demonstrate the feasibility of the proposed framework and its performance. Such a framework will motivate and incentivize cooperation among businesses and the crowd to assure traceability, cost-effectiveness, liability, and quality of service. 

Smart Efficient Energy Management System for Charging Stations Infrastructure (SEEMS-CSI)

There has been an increasing global interest in large-scale deployment of electric vehicles (EVs) and their charging stations. Electric vehicles offer many advantages including GHG emission reduction and better perfromance and efficiney. Several Countries have set policies to facilitate such large scale deployment. In 2018, the global electric car fleet exceeded 5.1 million.

The proposed project aims to develop an integrated smart energy management system that is capable of efficiently manage the energy consumption for a large-scale deployed electric vehicle charging station infrastructure including public and private station and enabling vehicle to grid discharge while considering both traffic and power network load profiles. The developed system will also investigate the optimal integration of mobile charging stations to the overall infrastructure.  

Research Staff and Graduate Students:

Dr. Maha Kadadha Post-Doc
Eng. Menatalla Abououf Research Engineer
Eng. Huda Abualola Research Associate
Eng. Mohammed Shurrab Research Associate
Ruba Nasser PhD candidate
Sani Umar PhD candidate

Several research Assisatant/Associate positions,  PhD and Msc projects as well as 2 post-doc positions are available in the Intelligent Crowd Group. Interested, please send an email to: ku.crowd.intelligence@gmail.com