Upload
lot
View
29
Download
2
Embed Size (px)
DESCRIPTION
SurroundSense: Mobile Phone Localization via Ambience Fingerprinting Ionut Constandache Co-authors: Martin Azizyan and Romit Roy Choudhury. Context. Pervasive wireless connectivity + Localization technology = Location-based applications. Location-Based Applications (LBAs). - PowerPoint PPT Presentation
Citation preview
1
SurroundSense: Mobile Phone Localization via Ambience Fingerprinting
Ionut Constandache
Co-authors: Martin Azizyan and Romit Roy Choudhury
2
Context
Pervasive wireless connectivity+
Localization technology=
Location-based applications
3
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
4
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
5
Location is an IP addressLocation is an IP addressAs if for content delivery
6
Thinking about Localizationfrom an application perspective…
7
Emerging location based apps need place of user, not physical location
Starbucks, RadioShack, Museum, Library
Latitude, Longitude
8
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 …
9
Can we convert from Physical to Logical Localization?
10
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
11
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
12
Several meters of error is inadequate to logically localize a phone
Physical LocationError
13
Several meters of error is inadequate to logically localize a phone
RadioShackStarbucks
Physical LocationError
The dividing-wall problem The dividing-wall problem
14
Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
15
Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
16
It is possible to localize phones by sensing the ambience
Hypothesis
such as sound, light, color, movement, WiFi …
17
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
18
SurroundSense
Multi-dimensional fingerprint Based on ambient sound/light/color/movement/WiFi
Starbucks
Wall
RadioShack
19
BB AACC DDEE
Should Ambiences be Unique Worldwide?
FFGG
HHJJ
II
LLMMNN
OO
PPQQ
RR
KK
20
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
21
++
Ambience Fingerprinting
Test Fingerprint
Sound
Acc.
Color/Light
WiFi
LogicalLocation
Matching
FingerprintDatabase
==
Candidate Fingerprints
GSM Macro Location
SurroundSense Architecture
22
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
23
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Moving
24
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Moving
Queuing
25
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Queuing Seated
Moving
26
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Pause for product browsing
Moving
27
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Pause for product browsing
Short walks between product browsing
Moving
28
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Walk more
Moving
29
Fingerprints
Movement: (via phone accelerometer)
Cafeteria Clothes Store Grocery Store
Static
Walk more Quicker stops
Moving
30
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
31
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
32
Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
33
Evaluation Methodology
51 business locations 46 in Durham, NC 5 in India
Data collected by 4 people 12 tests per location
Mimicked customer behavior
34
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
35
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
36
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
37
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
38
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
39
Evaluation: Per-Scheme Accuracy
Mode WiFi Snd-Acc-WiFi Snd-Acc-Lt-Clr SS
Accuracy 70%
74% 76% 87%
40
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
41
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:
42
Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
43
Limitations and Future Work
Energy-Efficiency
Localization in Real Time
Non-business locations
44
Limitations and Future Work
Energy-Efficiency Continuous sensing likely to have a large energy draw
Localization in Real Time
Non-business locations
45
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
46
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
47
Contents
SurroundSense
Evaluation
Limitations and Future Work
Conclusion
48
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
49
Questions?
Thank You!
Visit the SyNRG research group @http://synrg.ee.duke.edu/