Location of mobile computers, potentially indoors, is essential information to enable locationaware
applications in wireless pervasive computing. The popularity of wireless local area networks (WLANs) inside and around buildings makes positioning systems based on readily available received signal strength (RSS) from access points (APs) desirable. The fingerprinting technique associates location-dependent characteristics such as RSS values from multiple APs to a location (namely location fingerprint) and uses these characteristics to infer the location. The collection of RSS fingerprints from different locations are stored in a database called radio map, which is later used to compare to an observed RSS sample vector for estimating the MSs location.
An important challenge for the location fingerprinting is how to efficiently collect fingerprints
and construct an effective radio map for different indoor environments. In addition, analytical models to evaluate and predict precision performance of indoor positioning systems based on location fingerprinting are lacking. In this dissertation, we provide a location fingerprint framework that will enable a construction of efficient wireless indoor systems. We develop a new analytical model that employs a proximity graph for predicting performance of indoor positioning systems based on location fingerprinting. The model approximates
probability distribution of error distance given a RSS location fingerprint database and its associated statistics. This model also allows a system designer to perform analysis of the internal structure of location fingerprints. The analytical model is employed to identify and eliminate unnecessary location fingerprints stored in the radio map, thereby saving on computation while performing location estimation. Using the location fingerprint properties such as clustering is also shown to help reduce computational effort and create a more scalable model. Finally, by study actual measurement with the analytical results, a useful guideline for collecting fingerprints is given.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-10132008-122243 |
| Date | 08 January 2009 |
| Creators | Swangmuang, Nattapong |
| Contributors | Dr. Prashant Krishnamurthy, Dr. Ching-Chung Li, Dr. Hassan Karimi, Dr. Martin Weiss, Dr. Richard Thompson |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-10132008-122243/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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