A Survey of Wi-Fi Sensing Techniques with Channel State Information

doi:10.12142/ZTECOM.202003009

ZTE Communications ›› 2020, Vol. 18 ›› Issue (3): 57-63.DOI: 10.12142/ZTECOM.202003009

• Review • Previous Articles Next Articles

A Survey of Wi-Fi Sensing Techniques with Channel State Information

CHEN Liangqin¹, TIAN Liping¹, XU Zhimeng¹, CHEN Zhizhang^1,²()

^1.College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
^2.Department of Electrical and Computer Engineering, Dalhousie University, Halifax B3H 4R2, Canada

Received:2020-07-14 Online:2020-09-25 Published:2020-11-03
About author:CHEN Liangqin received the Ph.D. degree in communication and information systems from Fuzhou University, China in 2018. Since 2005, she has undertaken teaching and research work at Fuzhou University. Her research interests include wireless signal transmission, wireless sensing and detection.|TIAN Liping received the Master’s degree in communication and information systems from Fuzhou University, China in 2013. She is currently pursuing the Ph.D. degree at Fuzhou University. Her research interests include wireless signal transmission and sensing.|XU Zhimeng received the B.Sc. degree in radio physics from Lanzhou University, China in 2002, the M.Sc. and Ph.D. degrees in information and communication engineering from Xidian University and Fuzhou University in 2005 and 2013, respectively. He was with the Department of Electrical and Computer Engineering at Dalhousie University, Canada as a postdoctoral fellow from 2016 to 2017 and was a visiting scholar with the Department of Technology at the University of Northern Iowa， USA from 2011 to 2012. His research interests include ultra-wideband technologies, wireless sensing technologies, and wireless information & power transfer technologies.|CHEN Zhizhang (chen@dal.ca) received the B. Eng. degree in radio engineering from Fuzhou University, China, the Master’s degree in radio engineering from Southeast University, China, and the Ph.D. degree in electrical engineering from the University of Ottawa, Canada. He is a professor and the former Head of the Department of Electrical and Computer Engineering, Dalhousie University, Canada. He has authored and co-authored over 450 journal and conference papers in computational electromagnetics, RF/microwave electronics, antennas and wireless technologies. He is the Fellow of the IEEE, the Canadian Academy of Engineering and the Engineering Institute of Canada.
Supported by:
National Natural Science Foundation of China (NSFC)(61401100);Natural Science Foundation of Fujian Province(2018J01805);Youth Research Project of Fujian Provincial Department of Education(JAT190011);Fuzhou University Scientific Research Fund Project(GXRC-18074)

Abstract

Abstract:

A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented. The principles of how channel state information (CSI) is used and how the Wi-Fi sensing systems operate are reviewed. It provides a brief introduction to the algorithms that perform signal processing, feature extraction and recognitions, including location, activity recognition, physiological signal detection and personal identification. Challenges and future trends of Wi-Fi sensing are also discussed in the end.

Key words: Wi-Fi sensing, channel state information, signal processing, classifications, feature extraction, positioning, location, recognitions

CHEN Liangqin, TIAN Liping, XU Zhimeng, CHEN Zhizhang. A Survey of Wi-Fi Sensing Techniques with Channel State Information[J]. ZTE Communications, 2020, 18(3): 57-63.

Figures/Tables 5

Figure 1 Processing flow chart of a Wi-Fi sensing system.

Figure 2 Illustration of the Widar tracking results presented in Ref. [15].

Figure 3 Typical signal variations and features of activity recognitions.

Figure 4 Recurrence plot of different subcarriers.

Figure 5 Respiration and heartbeat rate sensing of human in walking.

References 30

1	LU Y, LV S H, WANG X D, et al. A survey on Wi⁃Fi based human behavior analysis technology [J]. Chinese journal of computers, 2019, 42(2): 1–21. DOI: 10.11897/SP.J.1016.2019.00231 DOI
2	WANG J, ZHAO Y N, FAN X X, et al. Device⁃free identification using intrinsic CSI Features [J]. IEEE transactions on vehicular technology, 2018, 67(9): 8571–8581. DOI:10.1109/tvt.2018.2853185 DOI
3	MA Y S, ZHOU G, WANG S Q. WiFi sensing with channel state information [J]. ACM computing surveys, 2019, 52(3): 1–36. DOI:10.1145/3310194 DOI
4	HALPERIN D, HU W J, SHETH A, et al. Tool release [J]. ACM SIGCOMM computer communication review, 2011, 41(1): 53. DOI:10.1145/1925861.1925870 DOI
5	HE W F, WU K S, ZOU Y P, et al. WiG: WiFi⁃based gesture recognition system [C]//2015 24th International Conference on Computer Communication and Networks (ICCCN). Las Vegas, USA, 2015: 1–7. DOI:10.1109/icccn.2015.7288485 DOI
6	LI H, YANG W, WANG J X, et al. WiFinger: talk to your smart devices with finger⁃grained gesture [C]//The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA, 2016: 250–261. DOI:10.1145/2971648.2971738 DOI
7	WANG Y, YANG J, CHEN Y Y, et al. Tracking human queues using single⁃point signal monitoring [C]//Annual International Conference on Mobile Systems, Applications, and Services. New Hampshire, USA, 2014:42–54. DOI: 10.1145/2594368.2594382 DOI
8	MA Y S, ZHOU G, WANG S Q, et al. SignFi: sign language recognition using Wi⁃Fi [J]. ACM on interactive, mobile, wearable and ubiquitous technologies, 2018, 2(1): 1–21. DOI:10.1145/3191755 DOI
9	OHARA K, MAEKAWA T, MATSUSHITA Y. Detecting state changes of indoor everyday objects using Wi⁃Fi channel state information [J]. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2017, 1(3): 1–28. DOI:10.1145/3131898 DOI
10	SINGH U, DETERME J F, HORLIN F, et al. Crowd forecasting based on wifi sensors and lstm neural networks [J]. IEEE transactions on instrumentation and measurement, 2020: 1. DOI:10.1109/tim.2020.2969588 DOI
11	BAHL P, PADMANABHAN V N. Radar: an in⁃building RF⁃based user location and tracking system [C]//19th Annual Joint Conference of the IEEE Computer and Communications Societies. Tel Aviv, Israel, 2000:775–784.DOI:10.1109/infcom.2000.832252 DOI
12	KOTARU M, JOSHI K, BHARADIA D, et al. SpotFi: decimeter level localization using Wi⁃Fi [J]. ACM SIGCOMM Computer communication review, 2015, 45(4): 269–282. DOI:10.1145/2829988.2787487 DOI
13	SCHMIDT R. Multiple emitter location and signal parameter estimation [J]. IEEE transactions on antennas and propagation, 1986, 34(3): 276–280. DOI:10.1109/tap.1986.1143830 DOI
14	VASISHT D, KUMAR S, KATABI D. Decimeter⁃level localization with a single Wi⁃Fi access point [C]//The 13th USENIX Symposium on Networked Systems Design and Implementation. Santa Clara, USA, 2016: 165–178
15	QIAN K, WU C S, YANG Z, et al. Widar: decimeter⁃level passive tracking via velocity monitoring with commodity Wi⁃Fi [C]//ACM Mobihoc 2017. Chennai, India, 2017: 1–10. DOI: http://dx.doi.org/10.1145/3084041.3084067 DOI
16	QIAN K, WU C S, ZHANG Y, et al. Widar 2.0: passive human tracking with a single Wi⁃Fi link [C]//The 16th Annual International Conference on Mobile Systems. Applications, and Services. New York, USA, 2018: 1–12. DOI:10.1145/3210240.3210314 DOI
17	LU G Y, SONG J K. 3D image⁃based indoor localization joint with WiFi positioning [C]// ACM on International Conference on Multimedia Retrieval. New York, USA, 2018: 1–8. DOI:10.1145/3206025.3206070 DOI
18	GUO A Y, XU Z M, CHEN L Q. A human action recognition method based on Wi⁃Fi channel state information [J]. Chinese journal of sensors and actuators, 2019, 32(11): 1688–1693. DOI: 10.3969 /j.issn.1004-1699.2019.11.015 DOI
19	BU Q R, YANG G, MING X X, et al. Deep transfer learning for gesture recognition with WiFi signals [J]. Personal and ubiquitous computing, 2020. DOI:10.1007/s00779-019-01360-8 DOI
20	ARSHAD S, FENG C H, YU R Y, et al. Leveraging transfer learning in multiple human activity recognition using WiFi signal [C]//2019 IEEE 20th International Symposium on “A World of Wireless, Mobile and Multimedia Networks”. Washington, USA, 2019: 1–10. DOI:10.1109/wowmom.2019.8793019 DOI
21	WANG H, ZHANG D Q, MA J Y, et al. Human respiration detection with commodity Wi⁃Fi devices [C]//ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA, 2016: 25–36. DOI:10.1145/2971648.2971744 DOI
22	ZENG Y Z, PATHAK P H, MOHAPATRA P. WiWho: WiFi⁃based person identification in smart spaces [C]//15th ACM/IEEE International Conference on Information Processing in Sensor Networks. Vienna, Austria, 2016: 1–12. DOI: 10.1109/ipsn.2016.7460727 DOI
23	ZHANG J, WEI B, HU W, et al. WiFi⁃ID: human identification using WiFi signal [C]//2016 International Conference on Distributed Computing in Sensor Systems. Washington, USA, 2016: 1–8. DOI:10.1109/dcoss.2016.30 DOI
24	HONG F, WANG X, YANG Y N, et al. WFID: passive device⁃free human identification using Wi⁃Fi signal [C]//Mobiquitous’16. Hiroshima, Japan, 2016: 1⁃10. DOI: http://dx.doi.org/10.1145/2994374.2994377 DOI
25	WANG W, LIU A X, SHAHZAD M. Gait recognition using Wi⁃Fi signals [C]// UbiComp ’16. Heidelberg, Germany, 2016: 363–373. DOI: http://dx.doi.org/10.1145/2971648.2971670 DOI
26	WANG J, GAO Q H, PAN M, et al. Device⁃free wireless sensing: challenges, opportunities, and applications [J]. IEEE network, 2018, 32(2):132–137. DOI: 10.1109/MNET.2017.1700133 DOI
27	WANG W, LIU A X, SHAHZAD M, et al. Understanding and modeling of Wi⁃Fi signal based human activity recognition [C]//MobiCom’15. Paris, France, 2015: 65–76. DOI: http://dx.doi.org/10.1145/2789168.2790093 DOI
28	YANG Z, ZHENG Y, WU C S. Intelligent wireless sensing in the AIoT: features, algorithms, and data sets [J]. Communications of the CCF, 2020, 16(2): 50–56
29	ZHENG Y, ZHANG Y, QIAN K, et al. Zero⁃effort cross⁃domain gesture recognition with Wi⁃Fi [C]//17th Annual International Conference on Mobile Systems, Applications, and Services. New York, USA: 2019. DOI:10.1145/3307334.3326081 DOI
30	WANG J, GAO Q H, MA X R, et al. Learning to sense: deep learning for wireless sensing with less training efforts [J]. IEEE wireless communications, 2020, 27(3): 156–162. DOI:10.1109/mwc.001.1900409 DOI

[1]	DENG Letian, ZHAO Yanru. Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions [J]. ZTE Communications, 2023, 21(2): 11-17.
[2]	LI Daiyi, TU Yaofeng, ZHOU Xiangsheng, ZHANG Yangming, MA Zongmin. End-to-End Chinese Entity Recognition Based on BERT-BiLSTM-ATT-CRF [J]. ZTE Communications, 2022, 20(S1): 27-35.
[3]	LI Zhongya, CHEN Rui, HUANG Xingang, ZHANG Junwen, NIU Wenqing, LU Qiuyi, CHI Nan. SVM for Constellation Shaped 8QAM PON System [J]. ZTE Communications, 2022, 20(S1): 64-71.
[4]	ZHAO Zipiao, ZHAO Yongli, YAN Boyuan, WANG Dajiang. Auxiliary Fault Location on Commercial Equipment Based on Supervised Machine Learning [J]. ZTE Communications, 2022, 20(S1): 7-15.
[5]	GAO Zhengguang, LI Lun, WU Hao, TU Xuezhen, HAN Bingtao. A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems [J]. ZTE Communications, 2022, 20(4): 110-115.
[6]	XU Yongjun, YANG Zhaohui, HUANG Chongwen, YUEN Chau, GUI Guan. Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks [J]. ZTE Communications, 2022, 20(1): 36-47.
[7]	LIU Junyu, YANG Yongjian, WANG En. BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing [J]. ZTE Communications, 2021, 19(2): 20-28.
[8]	JI Hong, ZHANG Tianxiang, ZHANG Kai, WANG Wanyuan, WU Weiwei. Efficient Network Slicing with Dynamic Resource Allocation [J]. ZTE Communications, 2021, 19(1): 11-19.
[9]	DENG Xu, ZHU Lidong. Resource Allocation Strategy Based on Matching Game [J]. ZTE Communications, 2020, 18(4): 10-17.
[10]	JIANG Zhihui, HE Yinghui, YU Guanding. Joint User Selection and Resource Allocation for Fast Federated Edge Learning [J]. ZTE Communications, 2020, 18(2): 20-30.
[11]	SUN Lin, DU Jiangbing, HUA Feng, TANG Ningfeng, HE Zuyuan. Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection [J]. ZTE Communications, 2020, 18(2): 57-73.
[12]	ZHANG Pengyu, XIE Lifeng, XU Jie. Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC [J]. ZTE Communications, 2020, 18(2): 49-56.
[13]	WANG Jia, ZHAO Yilong, HUANG Xin, HE Guangqiang. High Speed Polarization-Division Multiplexing Transmissions Based on the Nonlinear Fourier Transform [J]. ZTE Communications, 2019, 17(3): 50-55.
[14]	FENG Hong, LI Xi, ZHANG Heli, CHEN Shuying, JI Hong. Energy-Efficient Wireless Backhaul Algorithm in Ultra-Dense Networks [J]. ZTE Communications, 2018, 16(2): 16-22.
[15]	JIN Yaqi, XU Xiaodong, TAO Xiaofeng. Multi-QoS Guaranteed Resource Allocation for Multi-Services Based on Opportunity Costs [J]. ZTE Communications, 2018, 16(2): 9-15.

A Survey of Wi-Fi Sensing Techniques with Channel State Information

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 30

Related Articles 15

Recommended Articles

Metrics