ECE Professor Yingying Chen received a new NSF grant under the Division of Information and Intelligent Systems (IIS) for the project “Efficient and Robust Multi-model Data Analytics for Edge Computing.” This is a three-year $600,000 research project collaborating with Temple University and New York Institute of Technology. Rutgers’ share in this project is $240,000.
Many advanced edge-computing applications rely on large-scale data analysis for high-level decision-making. Edge computing makes computing faster and more efficient because it takes place near the physical location of either the user or the data rather than sending all the information to the cloud. For example, augmented reality/virtual reality (AR/VR) applications process data from high-definition sensors (such as cameras, motion sensors, and microphones) to enable accurate and robust human-computer interactions. Drones and electric vehicles perform tracking, adjustments and obstacle recognition and avoidance via analyzing data at the level of the vehicle. However, the current ability to understand and manage various high-dimensional sensing data is obscured by significant knowledge and data gaps due to the heterogeneous edge device and environments, hindering the building of precise models for emerging edge computing applications using data analytics. One important trend in edge computing is utilizing artificial intelligence (AI) to extract complex knowledge from various sensor measurements for precise modeling. Most edge devices have limited computing and memory resources, making it challenging to perform sophisticated data analytics using AI while satisfying the time requirements of most applications. Therefore, a heuristic data analytic framework is needed to enable efficient and robust edge event prediction using multi-model learning on resource-constrained edge devices. The goal of this project is developing transformative machine-learning and data analytics technologies for enabling AI-based applications on resource-constrained edge computing devices (such as IoT devices, AR/VR headsets, and drones). The outcome of this project will advance data analytics and machine-learning research by deriving and integrating various high-dimensional sensing data from diverse data sources and building robust predictive models for generic edge-computing applications.
More details on the project can be found at the NSF page here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311596&HistoricalAwards=falseCongratulations to Yingying!