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Anonymous Localization of Wireless Terminals
in IndoorsShahrokh ValaeeWireless and Internet Research Lab (WIRLab)Dept of Electrical and Computer EngineeringUniversity of Torontowww.comm.utoronto.ca/~valaee
Joint work with Chen Feng, Anthea Au, Moshe Eizenman, Sameh Sorour, Sophia Reyes, Sam Markowitz, Deborah Gold, Keith Gordon
Opportunistic Localization Workshop - May 2012 [Valaee]
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Objective
To design an accurate indoor navigation system that can be easily deployed on commercially available mobile devices without any hardware modification.
Localization Off-line measurements (site survey) On-line localization
Coarse localization Fine localization
Motivation
Regulations: E911
Commercial: shopping mall advertisement
Assistive: visually challenged
precision
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Fingerprinting
Collect fingerprints and store
Measure and compare
What about privacy?
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Fingerprinting Collect fingerprints by measuring the
power from access points Store the results in a location server
The user: Fetches fingerprints from the server Measures the received power from
available access points Compares the measured power with
the fingerprints Privacy observed
Looking for a systematic solution
Opportunistic Localization Workshop - May 2012 [Valaee]
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A newly developed theory in signal processing for sampling and reconstruction of sparse signals.
Compressive Sensing
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Localization Block Diagram
Two phases: Offline Phase
Online Phase
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Offline Phase
Measure RSS from multiple AP and store the average and variance in the server
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Clustering of Fingerprints
Adjacent points have similar RSS readings
An exemplar can act as a representative for the cluster
Clustering reduces computational complexity
Clustering removes outliers
Opportunistic Localization Workshop - May 2012 [Valaee]
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Online Phase
Retrieve fingerprints from the server
Measure the RSS Compare to the
fingerprints in two steps Coarse localization
Fine localization
Localization Steps
Coarse Localization Find the clusters to
which I belong
Fine Localization Locate me inside the
clusters
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Anonymous Localization Localization should be done on
the mobile unit Simple localization algorithm
deployable on cellphones Possibly a three-way solution
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Two different IDs
Authentication
service
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Implementation Buildings
4th floor of Bahen Center, University of Toronto
Canadian National Institute for Blind (CNIB) Bayview Village Shopping Mall (North Toronto)
440,000 square feet, 110 stores
Device Windows Mobile Platform
PDA (HP iPAQ windows mobile 2003 pocket PC) Samsung Omnia II smartphone
Android Platform HTC
Testing stage 30 Subjects
15 testing group 15 control group
3 tests for each subject
Test # # of turns Distance (m)
1 2 53.6
2 2 29.4
3 0 30.8
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Conclusion A localization scheme for indoor environment using
RSS, Compressive Sensing and Affinity Propagation has been proposed
Localization is done in two steps: coarse localization and fine localization
The solution has been implemented and tested in real environment and used for localization, navigation, and object finding
Since localization is done on the mobile, privacy is satisfied.