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CSCE 5013: Hot Topics in Mobile and Pervasive Computing
Discussion of LOC1 and LOC2
Nilanjan Banerjee
Hot Topic in Mobile and Pervasive Computing
University of ArkansasFayetteville, AR
Acknowledgment: Romit Roychoudhuri for the slides
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LOC2: SurroundSense
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Location-Based Applications (LBAs)
For Example: GeoLife shows grocery list when near Walmart MicroBlog queries users at a museum Location-based ad: Phone gets coupon at Starbucks
iPhone AppStore: 3000 LBAs, Android: 500 LBAs
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Most emerging location based apps do not care about the physical location
GPS: Latitude, Longitude
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Most emerging location based apps do not care about the physical location
Instead, they need the user’s logical location
GPS: Latitude, Longitude
Starbucks, RadioShack, Museum, Library
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Physical Vs Logical
Unfortunately, most existing solutions are physical
GPS GSM based Google Latitude
RADAR Cricket …
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Given this rich literature,
Why not convert from Physical to Logical Locations?
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Physical LocationError
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Pizza HutStarbucks
Physical LocationError
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Pizza HutStarbucks
Physical LocationError
The dividing-wall problem The dividing-wall problem
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SurroundSense:A Logical Localization Solution
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It is possible to localize phones by sensing the ambience
It is possible to localize phones by sensing the ambience
Hypothesis
such as sound, light, color, movement, WiFi …
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It is possible to localize phones by sensing the ambience
It is possible to localize phones by sensing the ambience
Hypothesis
such as sound, light, color, movement, WiFi …
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Multi-dimensional sensing extracts more ambient information
Any one dimension may not be unique, but put together, they may provide a
unique fingerprint
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SurroundSense
Multi-dimensional fingerprint Based on ambient
sound/light/color/movement/WiFi
Starbucks
Wall
Pizza Hut
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
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BB AACC DDEE
Should Ambiences be Unique Worldwide?
FFGG
HHJJ
II
LLMMNN
OO
PPQQ
RR
KK
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Should Ambiences be Unique Worldwide?
BB AACC DDEE
FFGG
HHJJ
II
KKLL
MMNNOO
PPQQ
RR
GSM provides macro location (strip mall) SurroundSense refines to Starbucks
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Economics forces nearby businesses to be diverse
Not profitable to have 3 adjascent coffee shopswith same lighting, music, color, layout, etc.
SurroundSense exploits this ambience diversity
Why does it work?
The Intuition:
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++
Ambience Fingerprinting
Test Fingerprint
Sound
Acc.
Color/Light
WiFi
LogicalLocation
Matching
FingerprintDatabase
==
Candidate Fingerprints
GSM Macro Location
SurroundSense Architecture
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Fingerprints
Sound:(via phone microphone)
Color:(via phone camera)
Amplitude Values-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
N
orm
aliz
ed
C
ount
0.14
0.12
0.1
0.08
0.06
0.04 0.02
0
Acoustic fingerprint (amplitude distribution)
Color and light fingerprints on HSL space
Lightn
ess
1
0.5
0
Hue
0
0.5
1 00.2
0.4
0.6
0.8
1
Saturation
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Fingerprinting Sound
Fingerprint generation : Signal amplitude Amplitude values divided in 100 equal intervals Sound Fingerprint = 100 normalized values
• valueX = # of samples in interval x / total # of samples
Filter Metric: Euclidean distance Discard candidate fingerprint if metric > threshold г
Threshold г Multiple 1 minute recordings at the same location di = max dist ( any two recordings )
г = max ( di of candidate locations )
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Fingerprinting Color
Floor Pictures Rich diversity across different locations Uniformity at the same location
Fingerprint generation: pictures in HSL space K-means clustering algorithm Cluster’s centers + sizes
Ranking metric
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Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Moving
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Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Queuing Seated
Moving
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Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Pause for product browsing
Short walks between product browsing
Moving
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Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Walk more Quicker stops
Moving
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Fingerprints
Movement: (via phone accelerometer)
WiFi: (via phone wireless card)
Cafeteria Clothes Store Grocery Store
Static
ƒ(overheard WiFi APs)
ƒ(overheard WiFi APs)
Moving
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Fingerprinting WiFi
Fingerprint generation: fraction of time each unique address was overheard
Filter/Ranking Metric Discard candidate fingerprints which do not have similar
MAC frequencies
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Discussion
Time varying ambience Collect ambience fingerprints over different time
windows
What if phones are in pockets? Use sound/WiFi/movement Opportunistically take pictures
Fingerprint Database War-sensing
