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

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Mobile Phone Localization via AmbienceFingerprinting

Scott Seto

CS 495/595

November 1, 2011


  • Mobile phones are becoming people-centric

  • Location-basedadvertisingiscomingsoon

  • There is an absense of well-establishedlogicallocalizationschemes

  • Physicallocalizationdoes not workwellindoors

What is surroundsense

  • 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


  • Installinglocalizationequipment in every area isunscalable

  • A schemewithaccuracy of 5 metersmay not place a person on the correct side of a wall


  • Fingerprintsfromvariousshopsvary over time

  • Colorsmaybedifferentbased on daylight or electric light

  • A soundfingerprintfrom a busyhourmight not match a low-activityperiod

Surroundsense architecture
SurroundSense Architecture

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

  • 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


  • 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


  • 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


  • 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