Efficient RF-based Location Sensing

PhD Thesis Proposal Defence


Title: "Efficient RF-based Location Sensing"

by

Mr. Jiajie TAN


Abstract:

Location sensing refers to detecting the presence or the positions of targets 
via sensors. Radio frequency (RF) technology, such as Wi-Fi and Bluetooth, has 
been proved effective and promising for location sensing due to its low cost, 
widely available infrastructure, and flexible deployment. In this thesis, we 
consider device-based location sensing where RF devices are associated with 
targets for sensing purposes. We tackle critical challenges for the efficient 
deployment of RF sensing systems. Our preliminary experimental results have 
been shown to be very encouraging and promising.

First, we study the issue of MAC address randomization in Wi-Fi-based people 
sensing. Such randomization breaks user path semantics, leading to difficulty 
in trajectory analytics. We propose an efficient association algorithm that 
recognizes the common emitters in a set of probe requests with randomized MAC 
addresses while preserving user privacy. To this end, we estimate the 
correlation between any two frames by considering their multimodalities such as 
information elements, sequence numbers, and received signal strength. With 
frames as nodes and correlation as edge cost, we then model the frame 
association problem as a minimum-cost flow optimization in a flow network. Our 
results are shown to be effective and able to associate frames of common 
emitters with high accuracy.

Second, we study how to overcome the blind spots in sensing infrastructure to 
achieve large-scale tracking. We propose a novel cooperative tracking system 
using mobile sensors to greatly expand the sensing coverage for cost-effective 
deployment. In the system, targets carry low-cost RF tags which not only beacon 
their IDs but also receive and rebroadcast beacons of other tags within a 
certain hop away. Mobile sensors, equipped with localization and communication 
modules, are used to capture and forward the beacons to a server to track the 
targets. To enhance sensing accuracy, we further introduce a matrix of received 
signal strength (RSS) to capture complex signal propagation, and jointly 
consider temporal and spatial information to more accurately track targets 
using a modified particle filter.

Third, to eliminate the site-survey overhead for fingerprint-based location 
sensing, we propose an implicit multimodal crowdsourcing method to 
automatically construct RF and geomagnetic fingerprint databases. We consider 
the spatial correlation among RF, geomagnetic, and motion signals to mitigate 
the impact of sensor noise, achieving highly accurate and robust fingerprinting 
without any explicit manual intervention. Using dynamic programming and 
clustering techniques, we locate unlabeled signals on a given map and filter 
mislabeled signals efficiently.

Apart from the above, as a sensing application, we propose and study an 
automated IoT-based geofencing algorithm to cost-effectively monitor 
home-quarantined confinees to contain COVID-19 pandemic. Confinees wear 
waterproof Bluetooth wristbands which are uniquely paired with their 
smartphones. We consider the IDs of the environmental network facilities (Wi-Fi 
access points and cellular networks) as the home signature to make IN/OUT 
decision. Such an idea has been adopted and deployed by the Hong Kong 
government to enforce the home quarantine order for hundreds of thousands of 
visitors so far.


Date:			Thursday, 15 April 2021

Time:                  	2:00pm - 4:00pm

Zoom Meeting:
https://hkust.zoom.us/j/95747051558?pwd=ZUZXOWNTclRUc2ZRb04xTlJ6VHlXZz09

Committee Members:	Prof. Gary Chan (Supervisor)
 			Prof. Andrew Horner (Chairperson)
 			Prof. Cunsheng Ding
 			Dr. Wei Wang


**** ALL are Welcome ****