The focus of my research is to understand and enhance the usability and processing capabilities of tiny, energy harvesting, batteryless sensing, and computing devices to realize their full potential in our daily lives. While existing works on batteryless computing systems concentrate preliminary on the lower-level goals, e.g., execution progress and memory consistency, their prospect in the time-sensitive applications are yet to be explored. My work leverages the data processing and control layer of batteryless systems and ensures timely response by (1) developing a unified framework that integrates energy harvesting and real-time systems, and (2) engineering machine learning and computer vision algorithms to allow imprecise computing.
My work exploits the data processing and control layers of the commercially available systems to propose frameworks that enable already deployed systems to become more efficient and adaptive. Time-sensitive batteryless systems open up new application domains including wildlife tracking, infrastructure monitoring, wearables, and healthcare. The interdisciplinary nature of my research involves a blend of diverse domains, including embedded systems, mobile computing, machine learning, mobile health, signal processing, and ubiquitous computing. Along with novel frameworks designing for time-aware intermittent systems, my research explored the sensing potential of tiny intelligent computing devices in various application domains including computer vision, healthcare, pedestrian safety, and infrastructure monitoring with a vision to merge these two paths in future works.