Type
Text
Type
Thesis
Advisor
Samir Das. | Luis Ortiz | IV Ramakrishnan.
Date
2011-05-01
Keywords
Computer Science | Expectation Maximization, Gaussian Mixture Models
Department
Department of Computer Science
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71610
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
We consider the problem of localizing a wireless client in an indoor environment based on the signal strength of its transmitted packets as received on stationary sniffers or access points. Current state-of-the art indoor localization techniques have the drawback that they rely extensively on a 'training phase'. This 'training' is a labor intensive process and must be done for each target-area under consideration for various device types. This clearly does not scale for large target areas. The introduction of unmodeled hardware with heterogeneous power-levels etc further reduces the accuracy of these techniques. We propose a solution in which we model the received signal strength as a Gaussian Mixture Model (GMM). We use expectation maximization to find the parameters of our GMM. We can now give a location fix for a transmitting device based on the maximum likelihood estimate. This way, we not only avoid the costly 'training phase' but also make our location estimates much more robust in the face of various form of heterogeneity and time varying phenomena. We present our results on two different indoor testbeds (CEWIT and Computer Science Buildings in Stony Brook University) with multiple WiFi devices (iphones, android phones, laptops, netbooks). We demonstrate that the accuracy is at par with state-of-the-art techniques but without requiring any training. We also show an application of such localization in extracting the hidden social structure of the occupants of the building based on their WiFi activity. We show interesting observations from the Computer Science building in Stony Brook University.
Recommended Citation
Goswami, Abhishek, "Wireless Indoor Localization using Expectation-Maximization on Gaussian Mixture Models" (2011). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 815.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/815