1 / 15

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

nia
Download Presentation

SurroundSense

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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?

More Related