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

• 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

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互连的唯一可寻址事物的全球网络。近年来，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测系统，该系统可能会继续流行，因为它以各种方式利用物联网来提高患者护理质量。该项目的成果将显著改善物联网中最先进的RF传感技术，并在以大幅降低成本的同时为丰富用户体验迈出重要一步。教育计划包括开发和加强该领域的本科和研究生课程。模拟工具和测试平台将为学生提供绝佳的机会，让他们获得尖端技术的实践经验。该项目的成果将通过出版物、专题介绍和项目网站传播。PI完全致力于增加代表性不足的群体在研究中的参与，并将继续通过外展，特别是通过与历史黑人学院和大学（HBCU）的合作来进一步推动这一努力。具体来说，该项目旨在通过利用先进的统计和学习技术，并开发有效的算法，使医疗物联网更加高效。这项研究旨在通过CSI张量和深度学习的智能探索，显着降低医疗物联网中RF感知的成本并提高其准确性。研究工作福尔斯分为以下三个相互交织的重点。(i)室内物联网环境中的RF感测：解决室内物联网环境中RF感测的几个基本挑战，包括为基于CSI的指纹识别开发随机模型，结合嵌套阵列，以及将深度学习应用于室内无设备定位。(ii)用于医疗保健的RF传感：利用射频和听不见的声音信号，为无接触和长期的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：我们将把前两个重点的组件集成到医疗物联网环境中，以了解这些组件的交互并评估整体系统性能。

项目成果

Dealing with Limited Backhaul Capacity in Millimeter-Wave Systems: A Deep Reinforcement Learning Approach

• DOI: 10.1109/mcom.2019.1800565
• 发表时间: 2018-12
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Mingjie Feng;S. Mao

TensorBeat: Tensor Decomposition for Monitoring Multiperson Breathing Beats with Commodity WiFi

• DOI: 10.1145/3078855
• 发表时间: 2017-10-01
• 期刊: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
• 影响因子: 5
• 作者: Wang, Xuyu;Yang, Chao;Mao, Shiwen
• 通讯作者: Mao, Shiwen

MiFi: Device-Free Wheat Mildew Detection Using Off-the-Shelf WiFi Devices

MiFi：使用现成的 WiFi 设备进行无设备小麦霉病检测

• DOI: 10.1109/globecom38437.2019.9013895
• 发表时间: 2019
• 期刊: 2019 IEEE Global Communications Conference (GLOBECOM
• 作者: Hu, Pengming;Yang, Weidong;Wang, Xuyu;Mao, Shiwen
• 通讯作者: Mao, Shiwen

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

• DOI: 10.1109/cc.2017.8107644
• 发表时间: 2017-10
• 期刊: China Communications
• 影响因子: 4.1
• 作者: Zaijian Wang;Yu-ning Dong;S. Mao;Xinheng Wang

Unsupervised Detection of Apnea Using Commodity RFID Tags With a Recurrent Variational Autoencoder

• DOI: 10.1109/access.2019.2918292
• 发表时间: 2019-05
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Chao Yang;Xuyu Wang;S. Mao