WiFi Sensing with Channel State Information: A Survey

被引:557
作者
Ma, Yongsen [1 ]
Zhou, Gang [1 ]
Wang, Shuangquan [1 ]
机构
[1] Coll William & Mary, Comp Sci Dept, 251 Jamestown Rd, Williamsburg, VA 23187 USA
基金
美国国家科学基金会;
关键词
WiFi sensing; channel state information; activity recognition; gesture recognition; human identification; localization; human counting; respiration monitoring; WiFi imaging; CSI; IDENTIFICATION; LOCALIZATION; FALL;
D O I
10.1145/3310194
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.
引用
收藏
页数:36
相关论文
共 154 条
[31]   From fresnel diffraction model to fine-grained human respiration sensing with commodity Wi-Fi devices [J].
Zhang, Fusang ;
Zhang, Daqing ;
Xiong, Jie ;
Wang, Hao ;
Niu, Kai ;
Jin, Beihong ;
Wang, Yuxiang .
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2 (01)
[32]  
Gao CH, 2018, PROCEEDINGS OF THE 15TH USENIX SYMPOSIUM ON NETWORKED SYSTEMS DESIGN AND IMPLEMENTATION (NSDI'18), P533
[33]   CSI-Based Device-Free Wireless Localization and Activity Recognition Using Radio Image Features [J].
Gao, Qinhua ;
Wang, Jie ;
Ma, Xiaorui ;
Feng, Xueyan ;
Wang, Hongyu .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (11) :10346-10356
[34]   An adaptive wireless passive human detection via fine-grained physical layer information [J].
Gong, Liangyi ;
Yang, Wu ;
Zhou, Zimu ;
Man, Dapeng ;
Cai, Haibin ;
Zhou, Xiancun ;
Yang, Zheng .
AD HOC NETWORKS, 2016, 38 :38-50
[35]   WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection [J].
Gong, Liangyi ;
Yang, Wu ;
Man, Dapeng ;
Dong, Guozhong ;
Yu, Miao ;
Lv, Jiguang .
SENSORS, 2015, 15 (12) :32213-32229
[36]  
Goodfellow I., 2014, arXiv 1406. 2661
[37]   MoSense: An RF-Based Motion Detection System via Off-the-Shelf WiFi Devices [J].
Gu, Yu ;
Zhan, Jinhai ;
Ji, Yusheng ;
Li, Jie ;
Ren, Fuji ;
Gao, Shangbing .
IEEE INTERNET OF THINGS JOURNAL, 2017, 4 (06) :2326-2341
[38]   HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data [J].
Guo, Linlin ;
Wang, Lei ;
Liu, Jialin ;
Zhou, Wei ;
Lu, Bingxian .
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2018,
[39]   WiFi-Enabled Smart Human Dynamics Monitoring [J].
Guo, Xiaonan ;
Liu, Bo ;
Shi, Cong ;
Liu, Hongbo ;
Chen, Yingying ;
Chuah, Mooi Choo .
PROCEEDINGS OF THE 15TH ACM CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS (SENSYS'17), 2017,
[40]   Tool Release: Gathering 802.11n Traces with Channel State Information [J].
Halperin, Daniel ;
Hu, Wenjun ;
Sheth, Anmol ;
Wetherall, David .
ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, 2011, 41 (01) :53-53