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Indoor localization using Wi-Fi fingerprinting

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Date Issued:
2013
Summary:
Nowadays the widespread availability of wireless networks has created an interest in using them for other purposes, such as localization of mobile devices in indoor environments because of the lack of GPS signal reception indoors. Indoor localization has received great interest recently for the many context-aware applications it could make possible. We designed and implemented an indoor localization platform for Wi-Fi nodes (such as smartphones and laptops) that identifies the building name, floor number, and room number where the user is located based on a Wi-Fi access point signal fingerprint pattern matching. We designed and evaluated a new machine learning algorithm, KRedpin, and developed a new web-services architecture for indoor localization based on J2EE technology with the Apache Tomcat web server for managing Wi-Fi signal data from the FAU WLAN. The prototype localization client application runs on Android cellphones and operates in the East Engineering building at FAU. More sophisticated classifiers have also been used to improve the localization accuracy using the Weka data mining tool.
Title: Indoor localization using Wi-Fi fingerprinting.
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Name(s): Mirzaei, Azandaryani Saeid, author
Cardei, Ionut E., Thesis advisor
College of Engineering and Computer Science, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: single unit
Date Created: Fall 2013
Date Issued: 2013
Publisher: Florida Atlantic University
Physical Form: Online Resource
Extent: 85 p.
Language(s): English
Summary: Nowadays the widespread availability of wireless networks has created an interest in using them for other purposes, such as localization of mobile devices in indoor environments because of the lack of GPS signal reception indoors. Indoor localization has received great interest recently for the many context-aware applications it could make possible. We designed and implemented an indoor localization platform for Wi-Fi nodes (such as smartphones and laptops) that identifies the building name, floor number, and room number where the user is located based on a Wi-Fi access point signal fingerprint pattern matching. We designed and evaluated a new machine learning algorithm, KRedpin, and developed a new web-services architecture for indoor localization based on J2EE technology with the Apache Tomcat web server for managing Wi-Fi signal data from the FAU WLAN. The prototype localization client application runs on Android cellphones and operates in the East Engineering building at FAU. More sophisticated classifiers have also been used to improve the localization accuracy using the Weka data mining tool.
Identifier: FA0004038 (IID)
Note(s): Includes bibliography.
Thesis (M.S.)--Florida Atlantic University, 2013.
Subject(s): Location based services
Mobile geographic information systems
Wireless LANs
Wireless communication systems
Held by: Florida Atlantic University Digital Library
Sublocation: Boca Raton, Fla.
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA0004038
Restrictions on Access: All rights reserved by the source institution
Restrictions on Access: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU