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WarnFi: non-invasive wifi-based abnormal activity sensing using non-parametric model

conference contribution
posted on 2017-01-01, 00:00 authored by N 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.)

Publisher

IEEE

Place of publication

Piscataway, N.J.