CAREER: Building Energy-Efficient IoT Frameworks - A Data-Driven & Hardware-Friendly Approach Tailored for Wearable Applications

Sensor energy efficiency is the top critical concern that hinders long-term monitoring in energy-constrained Internet-of-things (IoT) applications. Conventional compressive sensing techniques fail to achieve satisfactory performance in IoT and especially wearable applications due to the lack of prior knowledge about signal models and the overlook of individual variability. The research goal of this CAREER plan is to develop a data-driven and hardware-friendly IoT framework to fundamentally address the unmet energy efficiency need of IoT and especially wearable applications. This will be accomplished by a systematic approach that seamlessly integrates compressive sensing and data analytics in compressed domains using deep learning methods. The proposed research will provide a transformative IoT framework that significantly reduces the data size for transmission from sensors to cloud while improving the overall quality of information delivery and bringing signal intelligence closer to users. The research outcomes will directly impact a variety of IoT applications, such as long-term environmental sensing for monitoring the airborne quality, radiation, water quality, hazardous chemicals, and many other environment indicators, by allowing compressive sensors to be deployed in energy-constrained environments to perform precise information acquisition over a significantly increased time span impossible with existing technologies. The proposed framework will also advance wearable technologies to enable important progress in transforming the existing healthcare model from episodic examination for disease diagnosis and treatment to continuous monitoring for disease prediction and prevention. This will make our healthcare systems more effective and economic and improve the overall quality of living for billions of individuals. The PI will take advantage of his affiliation with the I/UCRC Center for Embedded Systems at ASU to engage industry sponsors to accelerate technology adoption and transfer to benefit the society at large. The PI also plans to undertake an ambitious education program to actively engage and impact a diverse population of K-12, undergraduate, and graduate students to take away the PI’s research and create more values for the community in the long term.

The specific research objectives are to 1) formulate problems and develop efficient solvers to construct binary near-isometry embedding matrices to enable effective data compression on sensors through compressive sampling; 2) train deep neuron networks to decode information directly from the compressive samples for on-chip data analytics; 3) prototype the proposed framework in wearable hardware and evaluate the system performance over a variety of physiological signals. The research outcomes will allow future IoT devices to precisely sense and transfer the information of interest specified by users in an energy-efficient manner rather than recording imprecise data in raw forms as in existing approaches. The findings from this research will advance the theory development of data-driven compressive sensing by filling the current knowledge gap on how to design near-isometry embedding matrices with binary constraints that are essential for cost-effective hardware mapping. It will also uncover the intrinsic connections between compressive sensing and deep learning by establishing a viable data analytics solution for decoding high-level information directly from compressive samples. On the integration of research and education, the PI will enhance the current curriculum to better prepare students for careers in both industry and academic. The PI will take advantage of the FURI program at ASU to engage undergraduate students in research to foster their interest and motivation to pursue graduate degrees. ASU has one of the largest Hispanic and Native American student populations in the nation. The PI will make strong personal efforts to encourage the recruitment, retention, and advancement of the underrepresented groups. The PI will also collaborate with the Fulton Engineering Education Outreach office to initiate an exciting high school teacher training program, which aims to increase the level of literacy and interest in STEM fields of a large body of high school students through advanced coursework development.