WiFiUS: RF Sensing in Internet of Things: When Deep Learning Meets CSI Tensor

物联网(IoT)是指基于标准通信协议的由相互连接的唯一可寻址的事物组成的全球网络。近年来，医疗保健被公认为物联网最具吸引力的应用领域之一，应用范围从医疗设施中的患者和设备跟踪到健康监测。该项目的目标是研究物联网医疗监控系统，随着它以各种方式利用物联网来提高患者护理质量，该系统可能会继续受到越来越多的欢迎。该项目的成果将显著提高物联网中射频传感的最新水平，并在大幅降低成本的情况下朝着丰富的用户体验迈出重要的一步。教育计划包括发展和加强该领域的本科生和研究生课程。模拟工具和试验台将为学生提供获得尖端技术实践经验的绝佳机会。该项目的成果将通过出版物、演示文稿和项目网站进行传播。PIs完全致力于让代表不足的群体更多地参与研究，并将继续通过外联进一步努力，特别是通过与历史上一直是黑人的学院和大学(HBCU)合作。具体地说，该项目旨在通过利用先进的统计和学习技术来深入了解医疗物联网中的射频传感，并开发有效的算法来提高医疗物联网的效率。本研究旨在通过CSI张量和深度学习的智能探索，显着降低医疗保健物联网射频传感的成本，提高射频传感的准确性。研究工作分为以下三个相互交织的阶段。(I)室内物联网环境中的射频传感：解决室内物联网环境中射频传感中的几个基本挑战，包括开发基于CSI的指纹识别随机模型、整合嵌套阵列以及应用深度学习进行室内无设备定位。(Ii)医疗射频传感：利用射频和听不见的声音信号，为非接触式和长期生命体征和人类行为监测开发有效的解决方案。(Iii)系统集成和评估：我们将把前两次推进中的组件集成到医疗物联网环境中，以了解这些组件的交互作用，并评估系统的整体性能。

Internet of Things (IoT) refers to a worldwide network of interconnected uniquely addressable things based on standard communication protocols. In recent years, medical care and healthcare are recognized as one of the most attractive application areas of the IoT, with applications ranging from patient and equipment tracking in medical facilities to health monitoring. The goal of this project is to investigate an IoT healthcare monitoring system, which is likely to continue to grow in popularity as it utilizes IoT in various ways for improving the quality of patient care. The outcomes from this project will significantly improve the state-of-the-art of RF sensing in the IoT, and provide a significant step toward enriched user experience at greatly reduced costs. The educational plan includes developing and enhancing undergraduate and graduate-level courses in the field. The simulation tool and testbed will provide excellent opportunities for students to gain hands-on experience in the cutting-edge technology. Outcomes from this project will be disseminated through publications, presentations, and a project website. The PIs are fully committed to increasing participation from under-represented groups in research, and will continue to further such efforts via outreach, in particular, through collaboration with Historically Black Colleges and Universities (HBCU).Specifically, this project aims to gain a deep understanding of RF sensing in healthcare IoT by exploiting advanced statistics and learning techniques, and to develop effective algorithms to make healthcare IoT more efficient. This research intends to significantly reduce the cost and improve the accuracy of RF sensing in healthcare IoT with intelligent exploration of CSI tensor and deep learning. The research work falls into the following three interwoven thrusts. (i) RF Sensing in Indoor IoT Environments: to address several fundamental challenges in RF sensing in indoor IoT environments, including developing a stochastic model for CSI based fingerprinting, incorporating nested arrays, and applying deep learning for indoor device-free localization. (ii) RF Sensing for Healthcare: to develop effective solutions for contact-free and long-term vital signs and human behavior monitoring, using RF and inaudible sound signals. (iii) System integration and evaluation: we will integrate the components from the first two thrusts into a healthcare IoT environment to understand the interaction of these components and evaluate the overall system performance.