THESIS
2015
xiii, 100 pages : illustrations ; 30 cm
Abstract
The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for sensing.
Sensing via WiFi enables remote sensing without wearable sensors and contactless sensing in
privacy-preserving mode, which are beneficial in various applications including security surveillance,
intrusion detection, elderly monitoring, and human-computer interaction. For WiFi sensing
to excel indoors, multipath propagation acts as a major concern. The multipath effect can invalidate
theoretical propagation models, distort received signal signatures, and constrain the performance
of wireless sensing even when inferring the presence of humans. To explicitly eliminate any adverse
impact of multipath propagation, researchers resort to customized signals and specialized
software-defined...[
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The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for sensing.
Sensing via WiFi enables remote sensing without wearable sensors and contactless sensing in
privacy-preserving mode, which are beneficial in various applications including security surveillance,
intrusion detection, elderly monitoring, and human-computer interaction. For WiFi sensing
to excel indoors, multipath propagation acts as a major concern. The multipath effect can invalidate
theoretical propagation models, distort received signal signatures, and constrain the performance
of wireless sensing even when inferring the presence of humans. To explicitly eliminate any adverse
impact of multipath propagation, researchers resort to customized signals and specialized
software-defined radios for radar signal processing. To enable device-free applications on commodity
infrastructures, existing approaches exploit a dense deployment of wireless links.
Instead of avoiding multipath, in this thesis, we demonstrate it is possible to harness multipath
in WiFi sensing with the PHY layer Channel State Information (CSI). First, we design a primitive
to identify the availability of the LOS path under multipath propagation with only commodity WiFi
devices to improve the multipath awareness in WiFi sensing. Second, we exploit the rich multipath
effect as fingerprints to blur the directional coverage of traditional passive human detection
architecture to achieve omnidirectional coverage. Third, we propose a measurable metric as proxy
for detection sensitivity and a lightweight subcarrier and path configuration scheme to adapt to
different multipath propagation conditions. Finally, we design a unified framework for both static
and moving human detection, by capturing the chest movements of static humans. We prototype
the above schemes with commodity WiFi infrastructure, and evaluate their performances in typical
office environments. Experimental results demonstrate improved detection accuracy, coverage and
sensitivity compared with MAC layer RSSI based schemes.
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