Mobile Phone Localization via Ambience Fingerprinting Written by Martin Azizyan Ionut Constandache amp Romit Choudhury Presented by Craig McIlwee Motivation Provide logical localization ID: 207528
Download Presentation The PPT/PDF document "SurroundSense" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
SurroundSense: Mobile Phone Localization via Ambience Fingerprinting
Written by Martin
Azizyan
,
Ionut
Constandache
, &
Romit
Choudhury
Presented by Craig
McIlweeSlide2
Motivation
Provide logical localization
Using GPS only isn’t good enough
Doesn’t work well indoors
Doesn’t account for dividing walls
Dedicated hardware is not scalableSlide3
Approach
Create an ambience fingerprint using sound, light, color, and user movement
Noise signatures specific to type of location/store
Chain stores have color themes
User movement indicative of store typeSlide4
Architecture/AlgorithmSlide5
Architecture/Algorithm
Data is recorded on the phone, preprocessed, and sent to a server
Filter module
Subsets the candidates
Wifi
, movement, sound
Match module
Selects the best candidate
Color/sound,
WifiSlide6
Architecture/Algorithm
No single module needs to be perfect
If each module is ‘good enough’ then all modules combined are sufficient
Being simple reasonably accurate instead of sophisticated and perfect reduces resources required for
processingSlide7
Sound Module
Filter
Sound varies over time
Fingerprints captured from various times of day
Similarity of fingerprints is used to create a threshold for a potential match
Match if within the threshold, discard otherwise
Threshold is generous
More false positives is better than false negativesSlide8
Motion Module
Filter
Variations in user behavior
Record 4 samples/second, use moving average over last 10 samples
Minor variations suppressedSlide9
Motion Module
User movement is classified as stationary or mobile
3 profiles defined
Long stationary – restaurant
Frequent movement with longer stationary – browsing
Frequent movement with shorter stationary – shopping
Some logical locations fit multiple profilesSlide10
Motion ModuleSlide11
Color/Light Module
Match
Images captured from camera while facing downward
Floor themes are consistent
Other orientations introduce noise
Common orientation when checking email, text messages,
etcSlide12
Color/Light Module
Analyze patterns in the image
First attempt was to convert pixels to RGB values
Failed due to shadow and reflection influences
Second attempt was to convert to HSL values
Isolates light on its own axisSlide13
Color/Light ModuleSlide14
Color/Light Module
Same/similar colors result in clusters when graphed
Dominant colors generate larger clusters
Similarity calculated as distance between cluster centroids and size of the clusters
Most similar candidate is the matchSlide15
Wifi Module
Normally a filter, match if camera is not available
Capture MAC address of available access points every 5 seconds
Compare occurrence ratio of currently available access points to known access pointsSlide16
Known Issues
Sound varies over time
Split day into 2 hour windows, capture fingerprints during each window
No mention of day of week, time of year
Camera in pocket
All testing done with phone in hand
Expected rise in wearable devices
Mimicking user behavior
Initial data showed artificial behavior
Subsequent attempts shadowed real customersSlide17
Known Issues
Resource (energy) intensive
Accelerometer fingerprint takes time to capture
Non-business locations may not exhibit enough diversity
Offices, airports, librariesSlide18
Evaluation
Recorded fingerprints of 51 locations
“War-sensed” by students
2 different groups during different times of day
Group A’s fingerprints used as database while Group B was at the location collecting their own fingerprints
Accuracy analysis was done on various combinations of sensors types
All sensor types combined yielded 87
% accuracy