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.

Cyber-Physical Systems (CPS) combine communication and information technology functions to the physical components of a system for purposes of monitoring, controlling, and automation. The power grid is becoming one of the largest CPS, where grid components are controlled based on the synergies in the cyberspace. CPS hold a great promise to improve the efficiency and productivity of numerous sectors in Canada and around the world.

The current generation of cellular networks, i.e., 4G, made possible multimedia applications such as music and video streaming in the palm of your hand. The upcoming generation of cellular networks, i.e., 5G, enables new types of applications beyond what 4G offered such as augmented virtual reality, Internet of Things, cloud computing, autonomous vehicles, connected health equipment and connected industrial robots. Communications with drones are expected to be one of the important applications of 5G networks.

The new generation 5G wireless networks will have a huge impact on the society due to the high bandwidth and capacities they provide. The traffic volume is expected to grow significantly and new varieties of applications, e.g., Internet of Things and vehicular networking, are anticipated. As a result, effective management of the new networks will become much more complicated and challenging. Machine learning techniques have made unprecedented progress in recent years, as they are highly efficient for data-driven applications.

G networks have emerged as a promising solution for Mobile Network Operators (MNOs) to offer ultra-fast mobile broadband and ultra-low latency services with exceptional reliability for consumers. By leveraging softwarization, Software-Defined Networking (SDN) and Network Function Virtualization (NFV), MNOs can offset the high capital and operational expenditures incurred due the additional deployment of legacy equipment.

Optical fiber communications systems are used throughout the global communications network to transmit information over distances ranging from several kilometers to thousands of kilometers. This infrastructure is the backbone of the Internet that is used on a daily basis worldwide. Applications driving demand for increased capacity include (i) video streaming services, (ii) cloud based storage and services, and (iii) machine-to-machine applications.

Data centers (DCs) in network softwarization and 5G eras are significantly different from those operated nowadays by public cloud providers. They are massively distributed, closer to end-users, heterogeneous (e.g., multi-access edge, central office as a data center, etc.) and rely on much more complex technologies (e.g., Network Functions Virtualization [NFV] and Software-Defined Networking [SDN]). This makes their Operation and Management (O&M) much more challenging. Much more intelligence is required for automating the various tasks.

Smartphone based indoor navigation services are desperately needed in an indoor GPS-denied environment, such as in Combat-zone Surveillance, Health Monitoring, Fire Detection, etc. The Receive Signal Strength (RSS) based algorithms are commonly used in indoor localization, which rely on the WiFi fingerprint data built by the Mobile Crowdsensing approach.