Boosting WiFi Sensing with Physical Layer Information

PhD Thesis Proposal Defence


Title: "Boosting WiFi Sensing with Physical Layer Information"

by

Mr. Zimu ZHOU


Abstract:

The growing PHY layer capabilities of WiFi has made it possible to reuse
WiFi signals for both communication and sensing. Sensing via WiFi enables
remote sensing without wearable sensors and contactless sensing in
privacy-preserving mode, which are beneficial in a range of 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 fundamentally 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 USRP radios for radar-like 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 study, we demonstrate it is
possible to harness multipath in WiFi sensing with the PHY layer Channel
State Information (CSI). In the first work, we design a pervasive
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. In the second work, we exploit the rich
multipath effect as fingerprints to blur the directional coverage of
traditional passive human detection architecture to achieve
omnidirectional coverage. In the third work, 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. We prototype the above three schemes with commodity WiFi
infrastructure, and evaluate their performances in typical office
environments. Experimental results demonstrate improved detection accuracy
and coverage even in multipath-dense scenarios compared with MAC layer
RSSI based schemes.


Date:			Friday, 6 February 2015

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                         lifts 25/26

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Qiong Luo (Chairperson)
 			Prof. Gary Chan
 			Dr. Ke Yi


**** ALL are Welcome ****