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

WiFiUS：物联网中的射频传感：当深度学习遇到 CSI Tensor

基本信息

批准号：
1702957
负责人：
Shiwen Mao
金额：
$ 30万
依托单位：
Auburn University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-15 至 2020-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702957&HistoricalAwards=false
关键词：
WiFiUS RF Sensing Internet Things

项目摘要

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.

物联网（IoT）是指基于标准通信协议的全球互联唯一解决方案网络。近年来，医疗保健和医疗保健被认为是物联网最具吸引力的应用领域之一，其应用程序从医疗机构中的患者和设备跟踪到健康监测。该项目的目的是调查物联网医疗保健监测系统，由于它以各种方式利用IoT来提高患者护理质量，因此该系统可能会继续受欢迎。该项目的结果将显着改善物联网中RF感测的最新水平，并为以大大降低的成本迈出了丰富的用户体验的重要一步。教育计划包括开发和增强该领域的本科和研究生课程。模拟工具和测试台将为学生提供极好的机会，以获得最先进技术的动手经验。该项目的结果将通过出版物，演示文稿和项目网站传播。 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.这项研究旨在通过明智地探索CSI张量和深度学习，从而大大降低成本并提高RF感知的准确性。研究工作属于以下三个交织的推力。（i）在室内IoT环境中的RF感应：解决室内IoT环境中RF感测的几个基本挑战，包括开发用于基于CSI的指纹的随机模型，结合嵌套阵列，并将深度学习应用于室内设备的无设置。（ii）用于医疗保健的RF感应：使用RF和听不清的声音信号开发有效的解决方案，用于无接触式和长期生命体征以及人类行为监测。（iii）系统集成和评估：我们将将从前两个推力的组件集成到医疗IoT环境中，以了解这些组件的相互作用并评估整体系统性能。