BOOSTING WIFI SENSING WITH PHYSICAL LAYER INFORMATION

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis 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 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 
commodityWiFi 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.


Date:			Monday, 7 December 2015

Time:			3:00pm - 5:00pm

Venue:			Room 2463
 			Lifts 25/26

Chairman:		Prof. Daniel Palomar (ECE)

Committee Members:	Prof. Prof. Lionel Ni (Supervisor)
 			Prof. Gary Chan
 			Prof. Ke Yi
 			Prof. Jianping Gan (MATH)
 			Prof. Qing Li (Comp. Sci., CityU)


**** ALL are Welcome ****