– M-MIMO Channel Estimation using Distributed Machine Learning and Edge Computing Technologies
Supported by Ericsson and OCE-VIP
– Spectrum Sharing with Machine Learning
Supported by Ericsson
– Contact Tracing System (Covid-19 Quarantine/Test Notification System)
Supported by NSERC Alliances Covid-19 Grant and CU Covid-19 Rapid Response Research Grant
– Cyber Security in Critical Infrastructures
Supported by NSERC Discovery Grant
A distributed system is a system whose components are located on different networked entities (e.g.,
computers, sensors) that communicate and coordinate their actions by exchanging messages. These
entities have a shared state, operate concurrently and can fail independently without affecting the whole
system’s uptime. With the ever-growing technological expansion of the world, distributed systems are
becoming more and more widespread.
In past years, I have addressed the protection and resilience of distributed systems such as Clouds, Data
Centres, Mobile Agent and Actuator Networks, Vehicular and Wireless Sensor Networks. Building on this
expertise, in the medium term, I plan to address various cyber security issues and develop attack resilient
solutions for a very important type of distributed systems, namely the industrial automated control
systems and networks that are monitoring and controlling the operation of critical infrastructures in
different sectors. These include the Energy sector (e.g., oil pipelines, nuclear plants), the Financial sector,
Government Operations, Water Supply, Health Systems and more. Cyberattacks against critical
infrastructures have increased significantly in the last decade and caused serious damage with very
important social and economic losses. However, research on protecting the control systems of critical
infrastructures against cyberattacks is still at an early stage and faces many open technical challenges.
– Distributed Computing
Supported by NSERC Discovery Grant
A. Secure Localization
B. Sensor Deployment in Complex Region of Interest
C. Security Threats in Clouds: Black Hole Search and Intrusion Detection using Mobile Agents
– Big Data Analytics
Supported by NSERC Discovery Grant
A. Big Data Analytics
Big data analytics is the process of examining (i.e., data mining) large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. There are two types of big data: data at rest (e.g., collection of what has streamed, web logs, emails, social media, unstructured documents and structured data from disparate system) and data in motion (e.g., twitter/facebook comments, stock market data and sensor data). Dealing effectively with big data despite its volume and variety requires efficient analysis of this data while it is still in motion, not just after it is at rest. Currently, there are essentially three approaches for Big Data Analytics: direct analytics over Massively Parallel Processing Data Warehouses, indirect analytics using Hadoop and direct analytics using Hadoop (which is a framework widely used in academia and industry in order to perform Big Data Analytics). With respect to Big Data Analytics, I am involved in the following projects:
a. Multiplayer Online Game Players’ Movement Prediction: an Effective Solution for Minimizing the Consequences of Poor Internet Quality
b. Social Network-based Data Dissemination in Vehicular Networks
B. Big Data Infrastructure
Work Distribution, Service Migration and Replica Placement algorithms and their simulations for MapReduce in the context of the Hadoop framework.
C. Big Data Applications: Information Security Issues Pertaining to Big Data in Healthcare
a. A Patient-Oriented Computer-Aided Approach to Improve Chronic Disease Care for Children
b. A Privacy-Protective Method for the Disclosure of Small Geographic Areas in Health Research
– Software Engineering
A. Reverse-Engineering Tests into Blueprint Requirements
B. Support for Non-Functional Requirements in Zeligsoft tools
C. Model-Based Acceptance Testing
