Migrating life-critical applications to wireless networks implies the increasing reliance of our society on continuous network connectivity. However, the sheer scale of networked diverse devices would increase the chance of accidental or intentional attacks. Signaling storm is a type of such attacks causing a denial of service through malicious/compromised devices attempting to attach to the network at a rate of thousands of times per hour. Unfortunately, the existing registration restriction mechanism can be bypassed by the remote exploitation of underlying vulnerabilities.

HTTP cookies are a small piece of data created by the web server to track visitors with goal of improving the users’ browsing experience. However, some companies make use the cookies to track internet users across multiple sites. This causes privacy concerns from the general public. The objective of this project is to develop a method to allow cookie synchronization without compromising individual privacy.

Distributed computing is a model in which components of a software system are shared among multiple computers to improve efficiency and performance. The growing interest in cloud computing scenarios that incorporate both distributed computing capabilities and heterogeneous hardware presents a significant opportunity for network operators. The aim of this Research is to develop a purpose-built discrete-event simulator for distributed quantum computing and identify further challenges and open problems arising from the design of a Distributed Quantum Computing ecosystem.

The increasing frequency of flooding has driven research to improve near real-time flood mapping from remote-sensing data. In Quebec, in the spring of 2017, several regions experienced severe flooding caused by consecutive record-setting rain events during snowmelt from early April to mi0-d-May. The current project aims to provide real-time monitoring tools not only for flooding but for drought as well, i.e., visualization and simulation tools using both remote sensing data, but also data collected from an Internet of Things network.

As Internet usages are proliferating communications networks are faced with new shortcomings. Future networks will have to support in 2020 mobile traffic volumes 1000 times larger than today and a spectrum crunch is anticipated. Wireless access rates are today significantly lower than those of fixed access, which prevents the emergence of ubiquitous low cost integrated access continuum with context independent operational characteristics. Communication networks energy consumption is growing rapidly, especially in the radio part of mobile networks.

The increasing frequency of flooding has driven research to improve near real-time flood mapping from remote-sensing data. In Quebec, in
the spring of 2017, several regions experienced severe flooding caused by consecutive record-setting rain events during snowsmelt from early
April to mi0-d-May. The current project aims to provide real-time monitoring tools not only for flooding but for drought as well, i.e.,
visualization and simulation tools using both remote sensing data, but also data collected from an Internet of Things network.

As the power processing density in new technologies such as 5G and 6G increases, reduction of ripples caused by switching power converter operation becomes a gradually more difficult design problem to solve. Analysis of these ripples and exploring various methods for the ripple reduction in the RF power amplifier circuits are the main topics for this project.

This project mainly introduces the ideas of blockchain and artificial intelligence algorithms into the future 6G Internet of Vehicles and Internet of Things environment. Try new breakthroughs against the current bottlenecks in the Internet of Vehicles and Internet of Things, and strive to improve the performance of the network communication environment.

In the recent years, blockchain technologies have shown promise as infrastructure for decentralized trustless anonymous digital asset exchange. The technology promises to transform how the data is shared in many areas including financial sector, insurance and gaming industries. Yet several obstacles prevent mainstream adoption of this technology - one of these challenges is security.

Anomaly detection or outlier detection is a technique to identify rare items, observations or events which are differing significantly from most of the data or do not conform to the expected behavior of the system. Typically, anomalous data cause numerous problems in the computer networking and communication system. This project aims to develop an advanced anomaly detection algorithm by utilizing state-of-the-art machine learning and artificial intelligence techniques and combining it with existing anomaly detection techniques.