Indoor Localization Using Wi-Fi Fingerprinting

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, K-Redpin, 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 iv 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. Localization using radio receivers to find the bearings of a radio transmitter with the use of simple triangulation was known and used since the invention of wireless communication. It was actually introduced in World War II to locate soldiers in emergency situations. In addition, Global Positioning System (GPS) was introduced during the Vietnam War and became available for commercial applications around 1990 [1]. Nowadays location based applications play an important role in wireless markets. Indoor positioning is motivated by a number of applications intended for commercial, public safety, event planning, and military uses [1]. Examples of these applications are using tracking, positioning, navigating, and some other personal uses. Most localization applications rely on the cellular network infrastructure meant for outdoor environments. However, ubiquity of wireless local area networks (WLAN) makes the opportunity to provide this services in indoor areas. Relying on the existent network infrastructure for finding a mobile device removes the need to specify a dedicated structure for this purpose that is very cost-effective. The main contribution of this thesis is the design, implementation, and performance evaluation of an indoor localization system for smartphone users that relies 2 on analyzing RF received signal strength (RSS) from the WLAN access points near users. The fundamental principle I applied for localization is that the ranges of signal values from individual access points seen as a multidimensional vector vary from place to place, such that …

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