he Central Virginia Node (CVN) has been a hub for collaborative research in cyber-physical systems, internet of things (IoT), and machine learning. In 2023, CVN researchers made significant contributions in several key areas, including:
- Weakly-supervised federated graph learning: This research area focuses on developing machine learning models that can learn from decentralized data sources, while preserving privacy. CVN researchers have made progress in this area by developing novel algorithms that can effectively leverage graph-structured data.
- GNNs for smart infrastructure systems: Graph neural networks (GNNs) are a powerful class of machine learning models that can be used to analyze data on graphs. CVN researchers have been exploring the use of GNNs for a variety of smart infrastructure applications, such as traffic management and energy grid optimization.
- Machine learning based CAN bus data attack detection in autonomous vehicles: The Controller Area Network (CAN bus) is a critical communication protocol used in modern vehicles. CVN researchers have been developing machine learning-based methods for detecting cyberattacks on CAN bus data, which could help to improve the safety and security of autonomous vehicles.
These are just a few examples of the many exciting research projects that are underway at CVN. The node is a valuable resource for researchers in the Mid-Atlantic region and beyond, and its work is helping to advance the state-of-the-art in cyber-physical systems, IoT, and machine learning.
