Internet of Things, Cyber Security, Connected Health
AI/Machine Learning, On-device/Edge AI, Big Data
Wireless Communications, Networking and Multimedia Communications
IoT for Secure and Smart Connected Health
Smart and Connected Health (SCH) is the use of Internet, sensing, communications and intelligent techniques in support of healthcare applications. Internet of Things (IoT) systems such as Wireless body area network (WBAN) system with various types of biomedical sensors is one of key infrastructures of SCH and provide an opportunity to address issues in rapidly increasing mHealth/eHealth applications. However, there are significant challenges in this area, such as improving the performance of WBANs, analytics of large and continuous physiological data collected from biomedical sensors and predictive modeling, and securing data transmission and protecting data privacy, especially in mobile and wireless environments.
Enabling Machine Learning based Cooperative Perception with mmWave Communication for Autonomous Vehicle Safety
By understanding what and how data are exchanged among autonomous vehicles, from a machine learning perspective, it is possible to realize precise cooperative perception on autonomous vehicles, enabling massive amounts of sensor information to be shared amongst vehicles. Such an advance can be extremely useful to extend the line of sight and field of view of autonomous vehicles, which otherwise suffers from blind spots and occlusions. The extended field of view on autonomous vehicles will be beneficial at times when there are occlusions preventing a complete perception of the environment. This increase in situational awareness promotes safe driving over a narrow scope and improves traffic flow efficiency over an extended scope. The main research objective of this project is to understand the sensing and communication challenges to achieving cooperative perception among autonomous vehicles, and to use the insights thus gained to guide the design of suitable data exchange format, data fusion algorithms, and efficient millimeter wave vehicular communications. The proposed scalable feature map transmission mechanism jointly considers the application requirements, link and physical layer characteristics of millimeter wave links, enabling sensor data sharing on a massive scale among autonomous vehicles. The implemented system and evaluation platform will serve as a convincing proof-of-concept for the proposed solution, thus opening the door to widespread adoption of cooperative perception applications via millimeter wave communications in future vehicle networks.
Enabling Computationally Efficient Fuzzy Clustering for Distributed Big Data
This project develops a strong theoretical underpinning for fuzzy clustering for Big Data applications. Existing fuzzy clustering approaches lack computational efficiency when the data are distributed, non-normal and high-dimensional, with a mix of categorical and continuous variables and missing values, although no prior assumptions of statistical distributions are required. In this project, new approaches are developed to augment the efficiency of fuzzy clustering for distributed Big Data. The works in the project include: (1) developing computational efficient fuzzy clustering for distributed Big Data; (2) designing a framework for intelligent fuzzy clustering over distributed Big Data; (3) performance validations through both simulations and real data. This project produces algorithms, theoretical models, and guidelines for practical implementation to enable fuzzy clustering and develop components for the scientific research and engineering communities.