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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

SurroundSense

Mobile Phone Localization via AmbienceFingerprinting

Scott Seto

CS 495/595

November 1, 2011

http://scott-seto.com/surroundsense

introduction
Introduction
  • Mobile phones are becoming people-centric
  • Location-basedadvertisingiscomingsoon
  • There is an absense of well-establishedlogicallocalizationschemes
  • Physicallocalizationdoes not workwellindoors
what is surroundsense
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
motivation
Motivation
  • Installinglocalizationequipment in every area isunscalable
  • A schemewithaccuracy of 5 metersmay not place a person on the correct side of a wall
challenges
Challenges
  • Fingerprintsfromvariousshopsvary over time
  • Colorsmaybedifferentbased on daylight or electric light
  • A soundfingerprintfrom a busyhourmight not match a low-activityperiod
detecting sound
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
detecting motion
Detecting Motion
  • People are stationary for a long period in restaurants and less in grocery stores
  • Place motion fingerprintsintobuckets
  • Differentiatebetweensitting and moving places
detecting color light
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
fingerprinting wifi
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
implementation
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
implementation1
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
future work
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
conclusion
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