WarnFi: non-invasive wifi-based abnormal activity sensing using non-parametric model
conference contribution
posted on 2017-01-01, 00:00authored byN Pang, D Zhu, Gang LiGang Li, S Liu
Abnormal activity sensing has attracted increasing research attention in military surveillance, patient monitoring, and health care of children and elderly, etc. Researchers have exploited the characteristics of wireless signals to sense 'keystrokes' and 'human talks', relieving the privacy invasion concern caused by mounting the surveillance cameras or wearing the smart devices. However, existing technologies usually require some specialized hardware, and can only sense a fixed set of pre-defined activities through a supervised learning from those wireless signals patterns. In this paper, we propose WarnFi, a non-invasive abnormal activity sensing system with only two commodity off-the-shelf (COTS) WiFi devices. The intuition of WarnFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the time-series of Channel State Information (CSI) will experience a unique variation. By using a non-parametric model, WarnFi can dynamically cluster the human body activities for abnormal sensing. Extensive experiments in various scenarios demonstrate the satisfactory performance of WarnFi.
History
Pagination
800-805
Location
Baltimore, Md.
Start date
2017-10-23
End date
2017-10-25
eISSN
2155-7586
ISBN-13
9781538605950
Language
eng
Publication classification
E Conference publication, E1 Full written paper - refereed
Copyright notice
2017, IEEE
Editor/Contributor(s)
Unknown
Title of proceedings
MILCOM 2017 : Proceedings of the 2017 IEEE Military Communications Conference
Event
IEEE Military Communications. Conference (2017 : Baltimore, Md.)