A RULE-BASED APPROACH TO INDOOR LOCALIZATION BASED ON WIFI SIGNAL STRENGTHS

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


PhD Thesis Defence


Title: "A RULE-BASED APPROACH TO INDOOR LOCALIZATION BASED ON
WIFI SIGNAL STRENGTHS"

By

Miss Qiuxia Chen


Abstract

Location plays a very important role in location-aware computing systems, 
in which objects are retrieved based on their physical locations. For 
example, finding the nearest objects around a person requires knowledge 
about the locations of the objects and the location of the person. The 
identification of the location of an object is known as localization. GPS 
(Global Positioning System) is widely used for localizing outdoor objects. 
Unfortunately, it does not work indoor because GPS signal cannot penetrate 
into buildings.

This thesis investigates localization methods in indoor environment. Since 
GPS is not available, a sensor infrastructure must be available to make 
indoor localization possible. This thesis focuses on approaches based on 
the Received Signal Strength (RSS) of WiFi signals because WiFi is widely 
available in indoor spaces. The main application scenario of this research 
is identify the location of a user inside a building. To achieve this 
goal, RSSs are measured at each location of a space and stored in the 
server. The measurements are called location signatures of the space. When 
localization is performed, the user obtains the RSS signature at her 
(unknown) location, and compares it with the location signatures at the 
server. The location with signature matching the user's signature the best 
is returned as the location of the user.

Traditional localization methods aim to improve localization accuracy, 
i.e., the error between the estimated location and the actual location. 
However, they assume that the location signatures are accurate. 
Unfortunately, RSSs are unstable due to noise, obstacles and environmental 
changes, causing localization accuracy to deteriorate quickly. Thus, 
regular calibration on the location signatures, which is prohibitively 
expensive, is required to maintain high localization accuracy.

This thesis aims to improve both the accuracy and the stability of indoor 
localization. Instead of using absolute RSSs in comparing the location 
signatures, we propose a rule-based approach to achieve high localization 
accuracy and stability. The main idea is to maintain the relations (i.e., 
“less than”', “equal to”, and “greater than”) of the RSSs of the access 
points (APs) received at a location and to set up rules to match the RSS 
signatures based on the relations. The rule-based approach enhances 
stability because the relation between two RSS signals could remain stable 
even when their values are changing constantly.

To further address the stability problem, we introduce two important 
notions, the stability and sensibility of an AP, at a location. Although 
the RSSs from APs change over time, some APs change less than the others, 
thus having higher stability, and some APs have stronger signals than the 
others, thus having higher sensibility. We introduce methods to estimate 
the stability and sensibility of APs. We present an effective and simple 
approach to create the relations and rules, as well as heuristics to 
select the rules for use in localization. We develop a suite of rule-based 
localization methods based on different combinations of the techniques, 
including pure matching of location signatures, rule-based system with and 
without AP stability, and rule-based systems with and without rule 
stability. We implemented the location methods and tested them in the 
Department's Lab area and the results show that rule-based system with 
stability consideration performs better that those without stability 
consideration, which in turn performs better than methods based on pure 
signature comparison.


Date:			Monday, 27 August 2012

Time:			10:00am – 12:00noon

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Wai-Ho Mow (ECE)

Committee Members:	Prof. Dik-Lun Lee (Supervisor)
 			Prof. Frederick Lochovsky
 			Prof. Raymond Wong
 			Prof. Albert Wong (ECE)
                         Prof. Hong-Va Leong (Comp., PolyU)


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