1 SurroundSense: Mobile Phone Localization via Ambience Fingerprinting

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SurroundSense: Mobile Phone Localization via Ambience Fingerprinting

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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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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 SkyHook 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


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Pizza HutStarbucks


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


Hypothesis

such as sound, light, color, movement, WiFi …

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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) decompressorare needed to see this picture.

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BB AACC DDEE

Should Ambiences be Unique Worldwide?

FFGG

HHJJ

II

LLMMNN

OO

PPQQ

QQ

RR

KK

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Should Ambiences be Unique Worldwide?

BB AACC DDEE

FFGG

HHJJ

II

KKLL

MMNNOO

PPQQ

QQ

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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Fingerprints

Movement: (via phone accelerometer)

Cafeteria Clothes Store Grocery Store

Static

Moving

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Fingerprints



Static

Queuing Seated

Moving

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Fingerprints



Static

Pause for product browsing

Short walks between product browsing

Moving

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Fingerprints



Static

Walk more Quicker stops

Moving

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Fingerprints


WiFi: (via phone wireless card)


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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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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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


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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


Sparse WiFi APs

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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


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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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Limitations and Future Work

Energy-Efficiency Continuous sensing likely to have a large energy

draw

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draw

Localization in Real Time User’s movement requires time to converge

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draw


Non-business locations Ambiences may be less diverse

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QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Electroic compasses can fingerprint layout Tables and shelves laid out in different orientations Users forced to orient in those ways

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.


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Ambience can be a great clue about locationAmbient Sound, light, color, movement …

None of the individual sensors good enoughCombined they may be unique

Uniqueness facilitated by economic incentive

Businesses benefit if they are mutually diverse in ambience

Ambience diversity helps SurroundSenseCurrent accuracy of 89%

Conclusion

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Questions?

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SurroundSense evaluated in 51 business locationsAchieved 89% accuracy

But perhaps more importantly, SurroundSense scales to any part of the world

Summary

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SurroundSense

Today’s technologies cannot provide logical localization

Ambience contains information for logical localization

Mobile Phones can harness the ambience via sensors More sensors, beter reliability

Evaluation results: 51 business locations, 87% accuracy

SurroundSense can scale to any part of the world

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Evaluation: Per-Shop Accuracy

Localization Accuracy per Shop

Average Accuracy (%) 0 10 20 30 40 50 60 70 80 90 100

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

C

DF

WiFiSnd-Acc-WiFiSnd-Acc-Clr-LtSurroundSense

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Architecture: Filtering & Matching

Candidate Fingerprints

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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 Motion

Fingerprint generation: stationary/moving periods Sitting (restaurants, cafes, haircutters ) Slow Browsing (bookstores, music stores, clothing) Speed-Walking (groceries)

Filter Metric:

Discard candidate fingerprints with different classification

static

moving

t

tR =

0.0 ≤ R ≤ 0.2 sitting0.2 ≤ R ≤ 2.0 slow browsing

2.0 ≤ R ≤ ∞ speed-walking

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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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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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Thinking about Localizationfrom an application perspective…

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Emerging location based apps need place of user, not physical location


Latitude, Longitude

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Emerging location based apps need place of user, not physical location


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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State of the Art in Physical Localization:1. GPS Accuracy: 10m 2. GSM Accuracy: 100m3. Skyhook (WiFi+GPS+GSM) Accuracy: 10m-

100m


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



100m


100m

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Physical Vs Logical

Lot of work in physical localization

GPS (5m accuracy, outdoor only) GSM based (high coverage, 200m accuracy) SkyHook (40m accuracy, need war-driving) Google Latitude (similar)

RADAR (5m accuracy, need indoor mapping) Cricket (install per-room devices) …

Each of them has distinct problems … Each of them has distinct problems …

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Physical Vs Logical

Lot of work in physical localization

GPS (5m accuracy, outdoor only) GSM based (high coverage, 200m accuracy) SkyHook (40m accuracy, need war-driving) Google Latitude (similar)

RADAR (5m accuracy, need indoor mapping) Cricket (install per-room devices) …

Each of them has distinct problems … Each of them has distinct problems …

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Even if we assume the best of all solutions

(5m accuracy indoors)

logical localization still remains a challenge.

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Contents

SurroundSense

Evaluation


Conclusion

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Contents

SurroundSense

Evaluation


Conclusion

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Contents

SurroundSense

Evaluation


Conclusion

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Contents

SurroundSense

Evaluation


Conclusion

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Contents

SurroundSense

Evaluation


Conclusion

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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


Multidimensional sensing

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Energy-Efficiency

Localization in Real Time

Non-business locations

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draw

Localization in Real Time


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draw



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Camera inside pockets Detect phone when out of pocket Takes pictures when camera pointing downward

Documents

1 SurroundSense: Mobile Phone Localization via Ambience Fingerprinting