Transcript Desiging a Virtual Information Telescope using Mobile Phones and Social Participation.

Desiging a Virtual Information Telescope
using Mobile Phones and Social Participation
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Virtual Information Telescope
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Context
Next generation mobile phones will have
large 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 world
to 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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A Virtual Information Telescope
Cloud
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Telescope
Web Service
Cloud
Virtual Telescope
Visualization Service
People
Phones
Physical Space
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Content Creation
Web Service
Cloud
Virtual Telescope
Visualization Service
People
Phones
Physical Space
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Content Retrieval
Web Service
Cloud
Virtual Telescope
Visualization Service
People
Phones
Physical Space
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MiroBlog Prototype
 Nokia N95 phones
 Symbian platform
 Carbide C++ code
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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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Telescope: Rich framework for applications and services
Apps
Virtual Information Telescope
People
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Free WiFi?
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Post-its in the air
 Dean’s
Office
Café 
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Tag View
Duke Trinity College
James Duke: Wanted to
donate to Princeton to
rename as Duke Univ.
John playing
frisbee
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Virtual Information Telescope
Apps
Research . . .
Location
HCI
Energy
Incentives
Mining
Privacy
Spam
Distillation
Scalability
...
People
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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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40
200
Research Question:
Can we achieve the best of both worlds
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Formulation
L(t0)
L(t2)
L(t3)
L(t4)
L(t1)
L(t5)
L(t6)
L(t7)
Accuracy
gain from GPS
Error
Accuracy
gain from WiFi
t0
t1
t2
t3
t4
GPS
t5
t6
WiFi
t7
Given energy budget, E, Trace T, and
location reading costs, egps , ewifi , egsm :
Schedule location readings to minimize avg. error
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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 necessary
Online optimal difficult
Need to design heuristics
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Our Approach
Do not invest energy if you can
predict (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
Prediction
generates
different error
curve
t0
t1
t2
t3
t4
t5
t6
t6
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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
Home
8:00
8:15
8:30
12:00
Road
crossing
8:05
12:05
Gym
 Exploit acclerometers
 Predict traffic turns
 Periodically localize to reset errors
Office
3:30
5:30
6:00
Library
6:00
Grocery
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Population Statistics
 Humans may deviate from mobility profile
 Predict based on 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
Starbucks
RadioShack
GPS Error
range
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Hypothesis
Its possible to localize phones by
sensing the ambience
such as sound, light, color, movement, orientation…
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SurroundSense
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Develop multi-modal fingerprint
 Using ambient sound/light/color/movement etc.
Starbucks
RadioShack
Wall
SurroundSense Server
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SurroundSense
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Each individual sensor not discriminating enough
Together, they are quite unique
 Use Support Vector Machines to identify uniqueness
Location
Classification
Algorithm (SVM)
Fingerprint
Database
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Should Ambiences be Unique Worldwide?
GSM provides macro location (mall)
SurroundSense refines to Starbucks
B
A
C
E
D
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Why will it work?
The Intuition:
Economics forces nearby businesses to be different
Not profitable to have 5 chinese restaurants
with same lighting, music, color, layout, etc.
SurroundSense exploits this ambience diversity
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Fingerprints
 Sound:
 Color:
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Fingerprints
 Light:
 Movement:
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Ambience Fingerprinting
Fingerprint
Filtering &
Matching
Sound
Color/Light
Test
Fingerprint
+
Compass
=
RF/Acc.
Macro
Location
Logical
Location
Fingerprint
Database
Candidate Fingerprints
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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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Summary
Ambience can be a great clue about location
Ambient Sound, light, color, movement …
None of the individual sensors good enough
Combined they may be unique
Uniqueness facilitated by economic incentive
Businesses benefit if they are mutually diverse in ambience
Ambience diversity helps SurroundSense
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Conclusion
 The Virtual Information Telescope
 A generalization of mobile, location
based, social computing
 Just developing apps
 Not enough
Internet
 Many challenges
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


Energy
Localization
Privacy
Incentives, data distillation …
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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
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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 at
http://synrg.ee.duke.edu
Thank You
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Several research challenges and opportunities
1. Energy-efficient localization
2. Symbolic localization through ambience sensing
3. Location privacy
4.
5.
6.
7.
Our Research
Incentives
Spam
Information distillation
User Inerfacing …
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Disclaimer
All of our projects are ongoing,
hence not fully mature
Today’s talk more about the problems
than about solutions
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Today’s Talk
Information
Telescope
Vision
System and
Applications
Challenges/Opporunities
Ongoing,
Future Work
1. EnLoc
2. SurroundSense
3. CacheCloak
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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
Web Service
Virtual Telescope
Cellular,
WiFi
Visualization Service
People
Phones
Physical Space
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Content Querying
Web Service
Virtual Telescope
Cellular,
WiFi
Visualization Service
People
Phones
Physical Space
Some queries participatory
Is beach parking available?
Others are not
Is there WiFi at the beach café?
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Content Floating [on physical space]
superb
sushi
Safe@
Nite?
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If designed carefully, a variety of
applications may emerge on Micro-Blog
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Free WiFi?
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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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Micro-Blog Beta live at
http://synrg.ee.duke.edu/microblog.html
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If designed carefully, the telescope
may enable a variety of applications
1. Location based RSS feeds
2. Post-its in the air
3. Tag View
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Thoughts
Micro-Blog:
Rich space for applications and services
But what are the research challenges here?
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Virtual Information Telescope
Apps
Research
Location
HCI
Energy
Incentives
Mining
Privacy
Spam
Distillation
Scalability
...
People
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