Transcript [Scott Seto]

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
• There is an absense of wellestablished logical localization
schemes
• Physical localization does not work
well indoors
What is SurroundSense?
• Uses the overall ambience of a place
to create a unique fingerprint for
localization
• Fingerprint location based on
ambient sound, light, color, RF, etc.
• Sensor data is distributed to
different modules
Motivation
• Installing localization equipment in
every area is unscalable
• A scheme with accuracy of 5 meters
may not place a person on the
correct side of a wall
Challenges
• Fingerprints from various shops vary
over time
• Colors may be different based on
daylight or electric light
• A sound fingerprint from a busy
hour might not match a low-activity
period
SurroundSense Architecture
Detecting Sound
• Ambient sound can be suggestive of
the type of place
• Use sound as a filter
• Eliminate outliers
• Compute the pair-wise Euclidean
distance between candidate and test
fingerprints
Detecting Motion
• People are stationary for a long
period in restaurants and less in
grocery stores
• Place motion fingerprints into
buckets
• Differentiate between sitting and
moving places
Detecting Color/Light
• Extract dominant colors and light
intensity from pictures of floors
• Translate the pixels to the huesaturation-lightness (HSL) to
decouple the actual floor colors
from the ambient light intensity
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
• 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
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
• 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
broader category
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
Questions?