Transcript Desiging a Virtual Information Telescope using Mobile Phones and Social Participation Romit Roy Choudhury Asst.

Desiging a Virtual Information Telescope
using Mobile Phones and Social Participation
Romit Roy Choudhury
Asst. Prof. (Duke University)
1
Virtual Information Telescope
2
Context
Next generation mobile phones will have
large number of sensors
Cameras, microphones, accelerometers, GPS,
compasses, health monitors, …
3
Context
Each phone may be viewed as
a micro lens
Exposing a micro view of the physical world
to the Internet
4
Context
With 3 billion active phones
in the world today
(the fastest growing comuting platform …)
Our Vision is …
5
A Virtual Information Telescope
Internet
6
One instantiation of this vision through
a system called Micro-Blog
- Content sharing
- Content querying
- Content floating
7
Content Sharing
Web Service
Virtual Telescope
Cellular,
WiFi
Visualization Service
People
Phones
Physical Space
8
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?
9
Content Floating [on physical space]
superb
sushi
Safe@
Nite?
10
If designed carefully, a variety of
applications may emerge on Micro-Blog
11
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
12
MiroBlog Prototype
 Nokia N95 phones
 Symbian platform
 Carbide C++ code
13
Micro-Blog Beta live at
http://synrg.ee.duke.edu/microblog.html
14
Prototype
15
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
16
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
17
Thoughts
Micro-Blog:
Rich space for applications and services
But where exactly is the research here ???!!**
18
Problem I
Energy Efficient Localization
(EnLoc)
19
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
20
Cost of Localization
 Performed extensive measurements
 GPS consumes 400 mW, AGPS marginally better
 Idle power consumption 55 mW
21
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 +
22
Tradeoff Summary:
20
40
200
Research Question:
Can we achieve the best of both worlds
23
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
24
Dynamic Program
 Minimize the area under the curve
 By cutting the curve at appropriate points
 Number of (GPS + WiFi + GSM) cuts must cost < budget
25
Offline optimal offers lower bound on error
Online algorithm necessary
Online optimal difficult
Need to design heuristics
26
Our Approach
Do not invest energy if you can
predict (even partially)
27
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
28
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
29
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
30
Buy Accuracy with Energy
 Comparison of optimal with simple interpolation
 GPS clearly not the right choice
31
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
32
Questions?
33
Problem 2
Symbolic localization
(SurroundSense)
34
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
35
Hypothesis
Its possible to localize phones by
sensing the ambience
such as sound, light, color, movement, orientation…
36
SurroundSense

Develop multi-modal fingerprint
 Using ambient sound/light/color/movement etc.
Starbucks
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
RadioShack
Wall
SurroundSense Server
37
SurroundSense


Each individual sensor not discriminating enough
Together, they are quite unique
 Use Support Vector Machines to identify uniqueness
Location
Classification
Algorithm (SVM)
Fingerprint
Database
38
Should Ambiences be Unique Worldwide?
GSM provides macro location (mall)
SurroundSense refines to Starbucks
B
A
C
E
D
39
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
40
Fingerprints
 Sound:
 Color:
41
Fingerprints
 Light:
 Movement:
42
Ambience Fingerprinting
QuickTim
e™ and a
TIFF(U ncompressed) decom
press or
are needed to see this pi cture.
Sound
Color/Light
Quic kTime™ a nd a
TIFF (Un co mp res sed ) d ec omp re sso r
ar e n eed ed to see thi s p ictu re.
Fingerprint
Filtering &
Matching
Test
Fingerprint
+
Compass
=
RF/Acc.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Macro
Location
Logical
Location
Fingerprint
Database
Candidate Fingerprints
43
Full System on Nokia N95
 Experimented on 58 stores
 10 different clusters
 Different parts of Duke campus
and in Durham city
44
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
45
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
Quick Time™ and a
TIFF ( Uncompr ess ed) decompr ess or
ar e needed to s ee this pic ture.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
a d na ™emi Tkci uQ
ro sser pmo ced ) des serp mocn U( F FIT
.erut cip s iht e es ot ded een era
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
46
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
47
Conclusion
 The Virtual Information Telescope
 A generalization of mobile, location
based, social computing
 Just developing apps
 Not enough
Internet
 Many challenges




Energy
Localization
Privacy
Incentives, data distillation …
48
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
49
PhonePoint Pens
 Using phone accelerometers
 To write short messages in the air
50
Please stay tuned for more at
http://synrg.ee.duke.edu
Thank You
51
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 …
52
Disclaimer
All of our projects are ongoing,
hence not fully mature
Today’s talk more about the problems
than about solutions
53
Today’s Talk
Information
Telescope
Vision
System and
Challenges/Opporunities
Applications
Ongoing,
Future Work
1. EnLoc
2. SurroundSense
3. CacheCloak
54