1 SurroundSense: Mobile Phone Localization via Ambience Fingerprinting Ionut Constandache Co-authors: Martin Azizyan and Romit Roy Choudhury
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SurroundSense: Mobile Phone Localization via Ambience Fingerprinting
Ionut Constandache
Co-authors: Martin Azizyan and Romit Roy Choudhury
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Context
Pervasive wireless connectivity+
Localization technology=
Location-based applications
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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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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
Location expresses context of user Facilitates content delivery
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Location is an IP addressLocation is an IP addressAs if for content delivery
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Thinking about Localizationfrom an application perspective…
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Emerging location based apps need place of user, not physical location
Starbucks, RadioShack, Museum, Library
Latitude, Longitude
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Emerging location based apps need place of user, not physical location
Starbucks, RadioShack, Museum, Library
Latitude, Longitude
We call this Logical Localization …We call this Logical Localization …
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Can we convert from Physical to Logical Localization?
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Can we convert from Physical to Logical Localization?
State of the Art in Physical Localization:1. GPS Accuracy: 10m 2. GSM Accuracy: 100m3. Skyhook (WiFi+GPS+GSM) Accuracy: 10m-
100m
State of the Art in Physical Localization:1. GPS Accuracy: 10m 2. GSM Accuracy: 100m3. Skyhook (WiFi+GPS+GSM) Accuracy: 10m-
100m
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Widely-deployable localization technologies have errors in the range of several meters
Widely-deployable localization technologies have errors in the range of several meters
Can we convert from Physical to Logical Localization?
State of the Art in Physical Localization:1. GPS Accuracy: 10m 2. GSM Accuracy: 100m3. Skyhook (WiFi+GPS+GSM) Accuracy: 10m-
100m
State of the Art in Physical Localization:1. GPS Accuracy: 10m 2. GSM Accuracy: 100m3. Skyhook (WiFi+GPS+GSM) Accuracy: 10m-
100m
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Several meters of error is inadequate to logically localize a phone
Physical LocationError
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Several meters of error is inadequate to logically localize a phone
RadioShackStarbucks
Physical LocationError
The dividing-wall problem The dividing-wall problem
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Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
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Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
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It is possible to localize phones by sensing the ambience
Hypothesis
such as sound, light, color, movement, WiFi …
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Sensing over multiple dimensions extracts more information from the ambience
Each 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
RadioShack
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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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++
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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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
Moving
Queuing
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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
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
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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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
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Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
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Evaluation Methodology
51 business locations 46 in Durham, NC 5 in India
Data collected by 4 people 12 tests per location
Mimicked customer behavior
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Evaluation: Per-Cluster Accuracy
Cluster
No. of Shops
1 2 3 4 5 6 7 8 9 10
4 7 3 7 4 5 5 6 5 5
A
ccura
cy (
%)
Cluster
Localization accuracy per cluster
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Evaluation: Per-Cluster Accuracy
Cluster
No. of Shops
1 2 3 4 5 6 7 8 9 10
4 7 3 7 4 5 5 6 5 5
A
ccura
cy (
%)
Cluster
Localization accuracy per cluster
Multidimensional sensing
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Evaluation: Per-Cluster Accuracy
Cluster
No. of Shops
1 2 3 4 5 6 7 8 9 10
4 7 3 7 4 5 5 6 5 5
Fault tolerance
A
ccura
cy (
%)
Cluster
Localization accuracy per cluster
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Evaluation: Per-Cluster Accuracy
Cluster
No. of Shops
1 2 3 4 5 6 7 8 9 10
4 7 3 7 4 5 5 6 5 5
A
ccura
cy (
%)
Cluster
Localization accuracy per cluster
Sparse WiFi APs
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Evaluation: Per-Cluster Accuracy
Cluster
No. of Shops
1 2 3 4 5 6 7 8 9 10
4 7 3 7 4 5 5 6 5 5
No WiFi APs
Acc
ura
cy (
%)
Cluster
Localization accuracy per cluster
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Evaluation: Per-Scheme Accuracy
Mode WiFi Snd-Acc-WiFi Snd-Acc-Lt-Clr SS
Accuracy 70%
74% 76% 87%
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Evaluation: User Experience
Random Person Accuracy
Average Accuracy (%)0 10 20 30 40 50 60 70 80 90 100
1 0.9 0.8 0.7 0.6 0.5
CD
F
0.4 0.3 0.2 0.1 0
WiFISnd-Acc-WiFiSnd-Acc-Clr-LtSurroundSense
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Economics forces nearby businesses to be different
Not profitable to have 3 coffee shopswith same lighting, music, color, layout, etc.
SurroundSense exploits this ambience diversity
Why does it work?
The Intuition:
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Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
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Limitations and Future Work
Energy-Efficiency
Localization in Real Time
Non-business locations
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Limitations and Future Work
Energy-Efficiency Continuous sensing likely to have a large energy draw
Localization in Real Time
Non-business locations
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Limitations and Future Work
Energy-Efficiency Continuous sensing likely to have a large energy draw
Localization in Real Time User’s movement requires time to converge
Non-business locations
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Limitations and Future Work
Energy-Efficiency Continuous sensing likely to have a large energy draw
Localization in Real Time User’s movement requires time to converge
Non-business locations Ambiences may be less diverse
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Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
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SurroundSense
Today’s technologies cannot provide logical localization
Ambience contains information for logical localization
Mobile Phones can harness the ambience through sensors
Evaluation results: 51 business locations, 87% accuracy
SurroundSense can scale to any part of the world
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Questions?
Thank You!
Visit the SyNRG research group @http://synrg.ee.duke.edu/
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