1 desiging a virtual information telescope using mobile phones and social participation romit roy...
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Desiging a Virtual Information Telescopeusing Mobile Phones and Social Participation
Romit Roy ChoudhuryAsst. Prof. (Duke University)
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Virtual Information Telescope
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Context
Next generation mobile phones will havelarge number of sensors
Cameras, microphones, accelerometers, GPS, compasses, health monitors, …
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Context
Each phone may be viewed as a micro lens
Exposing a micro view of the physical worldto the Internet
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Context
With 3 billion active phones in the world today
(the fastest growing comuting platform …)
Our Vision is …
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Internet Internet
A Virtual Information Telescope
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One instantiation of this vision through a system called Micro-Blog
- Content sharing- Content querying- Content floating
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Content Sharing
Virtual TelescopeVirtual Telescope
Cellular,WiFi
Cellular,WiFi Visualization ServiceVisualization Service
Web ServiceWeb Service
People
Physical SpacePhysical Space
Phones
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Content Querying
Virtual TelescopeVirtual Telescope
Cellular,WiFi
Cellular,WiFi Visualization ServiceVisualization Service
Web ServiceWeb Service
PhonesPeople
Physical SpacePhysical Space
Some queries participatoryIs beach parking available?
Some queries participatoryIs beach parking available?
Others are notIs there WiFi at the beach café?
Others are notIs there WiFi at the beach café?
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Content Floating [on physical space]
superb
sushi
Safe@
Nite?
Safe@
Nite?
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If designed carefully, a variety of applications may emerge on Micro-Blog
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Applications
Tourism View multimedia blogs … query for specifics
Micro Reporters News service with feeds from individuals
On-the-fly Ride Sharing Ride givers advertize intension w/ space-time sticky notes Respond to sticky notes once you arrive there
Virtual order on physical disorder Land in a new place, and get step by step information
RSS Feeds on Location Inform me when a live band is playing at the mall
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MiroBlog Prototype
Nokia N95 phones Symbian platform Carbide C++ code
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Micro-Blog Beta live athttp://synrg.ee.duke.edu/microblog.html
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Prototype
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Case Studies
Micro-Blog phones distributed to volunteers
12 volunteers• 4 phones in 3 rounds
• 3 weeks
Not great UI• Basic training for users
Exit interview revealed useful observations
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From Exit Interview
1. “Fun activity” for free time Needs much “cooler GUI”
2. Privacy control vital, don’t care about incentives “more interesting to reply to questions … interested in
knowing who is asking …”
3. Voice is personal, text is impersonal “Easier to correct text … audio blogs easier but …”
4. Logs show most blogs between 5:00 to 9:00pm Probably better for battery usage as well
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Thoughts
Micro-Blog:Rich space for applications and services
But where exactly is the research here ???!!**
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Problem I
Energy Efficient Localization(EnLoc)
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To GPS or not to GPS
GPS is popular localization scheme Good error characteristics ~ 10m
Apps naturally assume GPS Shockingly, first Micro-Blog demo lasted < 10 hours
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Cost of Localization
Performed extensive measurements GPS consumes 400 mW, AGPS marginally better Idle power consumption 55 mW
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Alternate Localization
WiFi fingerprinting, GSM triangulation Place Lab, SkyHook …
Improved energy savings WiFi 20 hours GSM 40 hours
At the cost of accuracy 40m + 200m +
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Tradeoff Summary:
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Research Question:Can we achieve the best of both worlds
200
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Given energy budget, E, Trace T, andlocation reading costs, egps , ewifi , egsm :
Schedule location readings to minimize avg. error
Given energy budget, E, Trace T, andlocation reading costs, egps , ewifi , egsm :
Schedule location readings to minimize avg. error
Formulation
L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
Error
t0 t1 t2 t3 t4 t5 t6
L(t5)
t7
Accuracy gain from GPS
Accuracy gain from WiFi
GPS WiFi
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Dynamic Program
Minimize the area under the curve By cutting the curve at appropriate points Number of (GPS + WiFi + GSM) cuts must cost < budget
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Offline optimal offers lower bound on error
Online algorithm necessaryOnline optimal difficult
Need to design heuristics
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Our Approach
Do not invest energy if you canpredict (even partially)
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Predictive Heuristics
Prediction opportunities exist Human users are not in brownian motion (exploit inertia) Exploit habitual mobility patterns Population distribution can be leveraged
Prediction also incorporated into Dynamic Program Optimal computed on a given predictor
Error
t0 t1 t2 t3 t4 t5 t6 t6
Predictiongenerates different errorcurve
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Mobility Profiling
Build logical mobility tree per-user Each link an uncertainty point (UP) Sample location only when uncertain Location predictable between UPs
Exploit acclerometers Predict traffic turns Periodically localize to reset errors
HomeHome
GymGym
Roadcrossing
Roadcrossing
LibraryLibrary
GroceryGrocery
OfficeOffice
8:008:15
8:3012:00
8:0512:05
3:305:30
6:006:00
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Humans may deviate from mobility profile
Predict based on population statistics
Population Statistics
Goodwin & Green
U-Turn Straight Right Left
E on Green 0 0.881 0.039 0.078
W on Green 0 0 0.596 0.403
N on Goodwin
0 0.640 0.359 0
S on Goodwin
0 0.513 0 0.486
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Buy Accuracy with Energy
Comparison of optimal with simple interpolation GPS clearly not the right choice
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Thoughts
Localization cannot be taken for granted Critical tradeoff between energy and accuracy
Substantial room for saving energy While sustaining reasonably good accuracy
However, physical localization May not be the way to go … Several motivations to pursue symbolic localization
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Questions?
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Problem 2
Symbolic localization(SurroundSense)
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Symbolic Localization
Services may not care about physical location Symbolic location often sufficient E.g., coffee shop, movie, park, in-car …
Physical to Symbolic conversion Lookup location name based on GPS coordinate However, risky
RadioShackStarbucks
GPS Errorrange
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Its possible to localize phones by sensing the ambience
such as sound, light, color, movement, orientation…
Hypothesis
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Develop multi-modal fingerprint Using ambient sound/light/color/movement etc.
Starbucks
SurroundSense Server
Wall
RadioShack
SurroundSense
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Each individual sensor not discriminating enough Together, they are quite unique
Use Support Vector Machines to identify uniqueness
FingerprintDatabase
ClassificationAlgorithm (SVM)
Location
SurroundSense
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BBAA
CC DD
EE
GSM provides macro location (mall)GSM provides macro location (mall)SurroundSenseSurroundSense refines to Starbucks refines to Starbucks
Should Ambiences be Unique Worldwide?
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Economics forces nearby businesses to be different
Not profitable to have 5 chinese restaurantswith same lighting, music, color, layout, etc.
SurroundSense exploits this ambience diversity
Why will it work?
The Intuition:
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Fingerprints
Sound:
Color:
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Fingerprints
Light:
Movement:
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++
Ambience Fingerprinting
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Test Fingerprint
Sound
Compass
Color/Light
RF/Acc.
LogicalLocation
Fingerprint Filtering &Matching
FingerprintDatabase
==
Candidate Fingerprints
Macro Location
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Full System on Nokia N95
Experimented on 58 stores 10 different clusters Different parts of Duke campus
and in Durham city
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Full System on Nokia N95
Some classifications were incorrect But we wanted to know how much incorrect? We plotted Top-K accuracy
Top-3 accuracy proved to be 100% for all stores
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Issues and Opportunity
Cameras may be inside pockets Now, we detect when its taken out Activate cameras, and take pictures Future phones will be flexible (wrist watch) - see Nokia
Morph
Electroic compasses can fingerprint layout Tables and shelves laid out in different orientations Users forced to orient in those ways
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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 SurroundSense
Summary
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Conclusion
The Virtual Information Telescope A generalization of mobile, location
based, social computing
Just developing apps Not enough
Many challenges Energy Localization Privacy Incentives, data distillation …
Internet Internet
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Conclusion
Project Micro-Blog Addressing the challenges systematically Building a fully functional system with applications
The project snapshot as of today, includes:
Micro-Blog: Overall system and application
EnLoc: Energy Efficient Localization
SurroundSense: Context aware localization
CacheCloak: Location privacy via mobility prediction
Micro-Blog: Overall system and application
EnLoc: Energy Efficient Localization
SurroundSense: Context aware localization
CacheCloak: Location privacy via mobility prediction
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PhonePoint Pens
Using phone accelerometers To write short messages in the air
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Please stay tuned for more athttp://synrg.ee.duke.edu
Thank You
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Several research challenges and opportunities
1. Energy-efficient localization2. Symbolic localization through ambience sensing3. Location privacy
4. Incentives5. Spam6. Information distillation7. User Inerfacing …
Our Research
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Disclaimer
All of our projects are ongoing, hence not fully mature
Today’s talk more about the problemsthan about solutions
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Today’s Talk
InformationTelescope
InformationTelescope
VisionVision System andApplicationsSystem andApplications
Challenges/OpporunitiesChallenges/Opporunities Ongoing, Future Work
Ongoing, Future Work
1. EnLoc2. SurroundSense3. CacheCloak