CPS: Breakthrough: Charge-Recycling based Computing Paradigm for Wirelessly Powered Internet-of-Things

Abstract: The primary objective of this research is to develop a new computing paradigm for wirelessly powered Internet-of-things (IoT) based devices and enhance their computational capabilities by more than an order of magnitude. The proposed research brings new opportunities to emerging applications such as computational RFIDs (Radio Frequency Identifiers), bio-implantable devices, and structural/environmental monitoring. The results of this research will contribute to the development of smarter IoT devices that are beyond the boundaries of traditional cyber-physical systems. Thus, significant new opportunities are expected in healthcare, energy, structural and environmental monitoring with substantial benefits to science, industry, and society at large. The PIs will promote outreach activities with emphasis on high school students and underrepresented groups. Undergraduate involvement will be encouraged through not only traditional means, but also through involvement of the PIs in professional societies and REU opportunities. One of the significant barriers that slows down the global scalability of IoT devices is the energy cost. Despite a variety of already existing techniques for energy harvesting methods, the computing capability of most of the IoT devices is limited by the available power. This research embodies a novel vision on developing an efficient computing method and corresponding IoT hardware architecture tailored for wireless energy harvesting. Specifically, the existing charge-recycling theory is leveraged in a unique way and the harvested AC signal is used to directly power the IoT device. Remarkable benefits are expected from this approach, e.g., i) the power loss due to rectification and regulation are eliminated, ii) the computational unit within the IoT device operates an order of magnitude more efficiently since the electrical charge is recycled rather than dissipated to ground. This unprecedented increase in energy efficiency enhances the on-site IoT device intelligence, thereby allowing for local decision making mechanisms.