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SurroundSense

SurroundSense. Mobile Phone Localization via Ambience Fingerprinting Scott Seto CS 495/595 November 1, 2011 http://scott-seto.com/surroundsense. Introduction. Mobile phones are becoming people- centric Location- based advertising is coming soon

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SurroundSense

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  1. SurroundSense Mobile Phone Localization via AmbienceFingerprinting Scott Seto CS 495/595 November 1, 2011 http://scott-seto.com/surroundsense

  2. Introduction • Mobile phones are becoming people-centric • Location-basedadvertisingiscomingsoon • There is an absense of well-establishedlogicallocalizationschemes • Physicallocalizationdoes not workwellindoors

  3. WhatisSurroundSense? • Uses the overallambience of a place to create a unique fingerprint for localization • Fingerprint location based on ambientsound, light, color, RF, etc. • Sensor data isdistributed to different modules

  4. Motivation • Installinglocalizationequipment in every area isunscalable • A schemewithaccuracy of 5 metersmay not place a person on the correct side of a wall

  5. Challenges • Fingerprintsfromvariousshopsvary over time • Colorsmaybedifferentbased on daylight or electric light • A soundfingerprintfrom a busyhourmight not match a low-activityperiod

  6. SurroundSense Architecture

  7. Detecting Sound • Ambientsoundcanbe suggestive of the type of place • Use sound as a filter • Eliminateoutliers • Compute the pair-wiseEuclidean distance between candidate and test fingerprints

  8. Detecting Motion • People are stationary for a long period in restaurants and less in grocery stores • Place motion fingerprintsintobuckets • Differentiatebetweensitting and moving places

  9. DetectingColor/Light • Extract dominant colors and light intensity from pictures of floors • Translate the pixels to the hue-saturation-lightness (HSL) to decouple the actual floor colors from the ambient light intensity

  10. Fingerprinting Wifi • Adapt existing WiFi based fingerprinting to suit logical localization • Use the MAC addresses of visible APs as an indication of the phone’s location • Avoid false negatives

  11. Implementation • Groups of students visited 51 stores using a Nokia N95 phone running SurroundSense • Collected fingerprints from each store • Visited each of them in groups of 2 people (4 people in total). • Keep the camera out of pocket

  12. Implementation • While in the store, try to behave like a normal customer • Went to different stores so that the fingerprints were time-separated • Mimiced the movement of another customer also present in that store • No atypical behavior: one may interpret the results to be partly optimistic

  13. Future Work • Independent research on energy efficient localization and sensing • Use the compass to correlate geographic orientation to the layout of furniture and shopping aisles in stores • Group logical locations into a broadercategory

  14. Conclusion • SurroundSense fingerprinted a logical location based on ambient sound, light, color, and human movement • Created a fingerprint database and performed fingerprint matching for test samples • Localization accuracy of over 85% when all sensors were employed for localization

  15. Questions?

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