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

在能源受限的物联网(IoT)应用中，传感器能效是阻碍长期监控的首要问题。由于缺乏信号模型的先验知识和对个体变异性的忽视，传统的压缩感知技术在物联网特别是可穿戴应用中无法获得令人满意的性能。这份职业规划的研究目标是开发一个数据驱动、硬件友好的物联网框架，从根本上解决物联网，尤其是可穿戴应用尚未满足的能效需求。这将通过一种系统的方法来实现，该方法使用深度学习方法在压缩领域中无缝地整合压缩感知和数据分析。拟议的研究将提供一个变革性的物联网框架，显著减少从传感器传输到云的数据大小，同时提高信息交付的整体质量，并使信号情报更接近用户。研究成果将直接影响各种物联网应用，例如用于监测空气质量、辐射、水质、危险化学品和许多其他环境指标的长期环境传感，允许在能源受限的环境中部署压缩传感器，以在现有技术无法大幅增加的时间跨度内执行精确的信息获取。拟议的框架还将推动可穿戴技术的发展，使现有的医疗模式从疾病诊断和治疗的间歇性检查转变为用于疾病预测和预防的持续监测，从而取得重要进展。这将使我们的医疗体系更加有效和经济，并提高数十亿人的整体生活质量。PI将利用他在亚利桑那州立大学I/UCRC嵌入式系统中心的联系，与行业赞助商合作，加快技术的采用和转让，以造福于整个社会。PI还计划实施一项雄心勃勃的教育计划，以积极参与和影响不同的K-12岁人群、本科生和研究生，以取代Pi？S的研究，并从长远来看为社区创造更多价值。具体研究目标是1)提出问题并开发高效的解算器，以构建二进制近等距嵌入矩阵，从而通过压缩采样在传感器上实现有效的数据压缩；2)训练深层神经元网络，以直接从压缩样本中解码信息，用于芯片上的数据分析；3)在可穿戴硬件中构建所提出的框架，并评估系统在各种生理信号下的性能。研究成果将使未来的物联网设备能够以节能的方式精确感知和传输用户指定的感兴趣的信息，而不是像现有方法那样以原始形式记录不准确的数据。这项研究的发现将通过填补目前关于如何设计具有二进制约束的近等距嵌入矩阵的知识空白来推动数据驱动压缩传感的理论发展，而二进制约束对于经济高效的硬件映射是必不可少的。它还将通过建立一个可行的数据分析解决方案来直接从压缩样本中解码高级信息，从而揭示压缩感知和深度学习之间的内在联系。在研究与教育的结合方面，创新进修学院会加强现行课程，为学生在工业界和学术界就业作好准备。PI将利用亚利桑那州立大学的FURI计划，让本科生参与研究，以培养他们攻读研究生学位的兴趣和动力。亚利桑那州立大学拥有全国最大的拉美裔和美洲原住民学生人口之一。国际和平协会将作出强有力的个人努力，鼓励招聘、留住和提升代表性不足的群体。PI还将与富尔顿工程教育外展办公室合作，启动一项令人兴奋的高中教师培训计划，旨在通过高级课程开发提高大量高中生在STEM领域的识字水平和兴趣。

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.