REU Site Renewal: Enhancing AI-Driven Insights Within IoT-Enabled Ecosystems (AIoT-Sys)

About This Grant

Artificial Intelligence (AI) and the Internet of Things (IoT) are reshaping the way people interact with technology. Everyday devices—from home sensors and wearable monitors to industrial systems—are now capable of gathering data and making decisions in real time. When AI is embedded into these connected systems, a powerful combination emerges: the ability to detect patterns, respond to changes, and automate functions that once required human oversight. This blend, known as AIoT, has the potential to improve safety, efficiency, and responsiveness across many sectors, including transportation, healthcare, and environmental monitoring. However, to fully realize these benefits, there is a growing need for well-prepared engineers and computer scientists who can design, analyze, and manage these complex systems. This project helps meet that need by offering research experiences to undergraduate students who may not otherwise have access to high-impact scientific opportunities. Through mentorship, project-based learning, and exposure to advanced tools, participants will gain the knowledge and confidence to pursue careers at the forefront of technological innovation. The project, titled “REU Site Renewal: Enhancing AI-Driven Insights Within IoT-Enabled Ecosystems (AIoT-Sys)”, is based at the University of California, Irvine and builds on prior success training students in IoT systems research. During the eight-week program, undergraduate participants will engage in applied research projects led by faculty from computer science, electrical engineering, and health sciences. Technical focus areas include: (1) the development of secure, low-latency communication protocols for wearable medical sensors and implantable monitoring devices; (2) the optimization of distributed energy management algorithms in building automation systems; and (3) the application of supervised and unsupervised machine learning techniques for the interpretation of large-scale environmental data collected from remote sensor networks. Students will perform a progression of tasks: reviewing existing literature, implementing prototypes using microcontroller-based hardware platforms, collecting and analyzing experimental data, and presenting their findings through oral presentations and written reports. Participants will design and evaluate their systems using professional-grade computing tools and cloud-based resources. The program also includes training sessions on technical communication, ethics in research, and strategies for pursuing graduate education. Assessment will be conducted through surveys, mentor evaluations, and tracking of participant outcomes to measure the impact on academic and career trajectories. By equipping students with foundational knowledge and practical experience in AI-integrated sensing and control systems, this program supports the development of a highly skilled technical workforce prepared to contribute to critical areas of science and engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.