The Importance of Being Wireless

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The Importance of Being Wireless
Romit Roy Choudhury
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The Context

• The edge of the internet becoming wireless
• 167,000 hotspots by 2008 end GartnerSurvey06
• 75 million user base
• Mesh extensions offered VoIP in rural regions
• Future predictions are unanimously positive
• Mobile phone sales will soon surpass computers
• OLPC Wireless will bridge the digital divide
• The Vision
• Lets make wireless like electricity
• Let everyone take it for granted

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Wireless Networking Mobile Computing
Internet
Offering information access anytime, anywhere
Mesh Networks
Personal Networks
RFID, Sensors Networks
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Miles To Go
Mobile Social Apps
Localization
Application
Privacy
Security
Eavesdropping
Loss Discrimination
Transport
Mobility
Network
Energy Savings
MAC / Link
Spatial Reuse
Interference Mgmt.
PHY
Channel fluctuations
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Webpage
http//synrg.ee.duke.edu
SyNRG
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Our Research
Application Driven Research (top down)
Application
Security
Transport
Network
MAC / Link
PHY
Exploiting PHY Layer Capabilities (bottom up)
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Selected Projects
Shuffle
Micro-Blog
Mobile Computing
Wireless Networking
Spotlight
Mingle
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Selected Projects
Shuffle
Micro-Blog
Mobile Computing
Wireless Networking
Spotlight
Mingle
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• Spotlight
• Exploiting Smart Antennas for Wireless Networks

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Wireless Networking Mobile Computing
Internet
Offering information access anytime, anywhere
Mesh Networks
Personal Networks
RFID, Sensors Networks
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Omnidirectional Antennas
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IEEE 802.11 with Omni Antenna
silenced
M
Interference management A crucial challenge
for dense multihop networks
S
Data
D
ACK
silenced
X
K
silenced
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Managing Interference

• Several approaches
• Dividing network into different channels
• Power control
• Rate Control

Recent Approach Exploiting antenna capabilities
to improve the performance of wireless multihop
networks
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From Omni Antennas (Bulbs)
silenced
M
S
D
silenced
X
K
silenced
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To Beamforming Antennas (Spotlights)
M
S
D
X
K
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To Beamforming Antennas (Spotlights)
M
S
D
X
K
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However
Throughput (Kbps)
Sending Rate (Kbps)
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Selected Projects
Shuffle
Micro-Blog
Mobile Computing
Wireless Networking
Spotlight
Mingle
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• Shuffle
• A New Way to Cope with Wireless Interference

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Successive Interference Cancellation (SIC)

• State of the art allows only one reception
• The stronger one
• SIC enables a receiver to receive both signals
• Stronger signal decoded and subtracted
• Residual signal decoded from the residue

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SIC based WLANs

• Existing schemes require SINR gt ?
• Game of out-shouting each other
• SIC offers payoff if transmitter
• Either out-shouts or whispers
• Fundamental changes for protocol design

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MIM SIC

• Ongoing work on GNU radios
• SIC MIM implementation can enable protocols

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Wireless BEN

• Questions / thoughts
• Should BEN venture into the wireless world
• Or is that something to build once we have the
  wired infrastructure
• Wireless networks have several interdependences
  with wired backend
• Important to take these issues during system
  design
• BEN has the potential to become one such system
• Wireless network management critical
• Wired network management tools/expertise is a new
  opportunity
• Can BEN be the bridge between research prototype
  and real deployments
• Disaster relief
• Rural internet access
• Healthcare and education

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Selected Projects
Shuffle
Micro-Blog
Mobile Computing
Wireless Networking
Spotlight
Mingle
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• Mingle
• Exploiting Social Behavior for Routing in
• Delay Tolerant Networks

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Delay Tolerant Networks

• Exploit mobility as an opportunity
• When wireless connection unavailable

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Selected Projects
Shuffle
Micro-Blog
Mobile Computing
Wireless Networking
Spotlight
Mingle
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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
Internet
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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 Telescope
Web Service
Cellular, WiFi
Visualization Service
People
Phones
Physical Space
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Content Querying
Virtual Telescope
Web Service
Cellular, WiFi
Visualization Service
People
Phones
Physical Space
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Content Floating on physical space
superb sushi
Safe_at_ 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
• Negotiate deal on third party server
• Virtual order on physical disorder
• Land in a new place, and get step by step
  information on your mobile

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• Micro-Blog Beta live at
• http//synrg.ee.duke.edu/microblog.html

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Prototype
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Thoughts

• Micro-Blog
• Rich space for applications and services

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• Several research challenges and opportunities
• Energy-efficient localization
• Symbolic localization through ambience sensing
• Location privacy
• Incentives
• Spam
• Information distillation
• User Inerfacing

Our Research
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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 lt 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
• 400m

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Tradeoff Summary
40
20
400
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Formulation
L(t2) L(t3) L(t4)
L(t0)
L(t1)
L(t6)
L(t5)
L(t7)
Error
t0
t1
t2
t3
t4
t5
t6
t6
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 lt
  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
• Exploit habitual mobility patterns / inertia
• 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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1. Simple Interpolation

• Take GPS reading, followed by WiFi
• Extrapolate GPS in the direction of WiFi
• Reset prediction after threshold time, take GPS
  again

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2. 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

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3. Exploit Group Behavior

• Characterize how masses behave at uncertainties
• Example At traffic intersections
• Predict individual mobility based on mass behavior

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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• Problem/Opportunity 2
• Symbolic localization via ambience sensing
• (SurroundSense AAMPL)

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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 possible
• Lookup location name based on GPS coordinate
• However, risky

Walmart
Starbucks
GPS Error range
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Our Approach

• Build symbolic localization algorithms
• Use low accuracy physical localization as
  baseline
• Low accuracy conserves energy

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SurroundSense

• Sense ambient light, sound, colors
• Combine sensor readings to generate soft
  fingerprint
• For localization, gather fingerprint from mobile
• Match with database of fingerprints
• Of course, fingerprint may not be globally unique
• Use rough physical localization as a pre-filter

GSM physical location says you are in the
mall SurroundSense augments that with you are
in Apple Store
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SurroundSense Design

• Prototype on Tmote Invent sensors
• Sound and light sensors
• Low acoustic frequency range 20, 250 Hz
• Currently porting on Nokia N95 phones

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Fingerprint Extraction

• Light intensity and sound signals recorded
• Fourier transform on sound
• Overlapping frequency blocks generated
• Each block 23 bands, 10 Hz each
• Extract simple features
• Each 10 Hz band one feature
• Variance of each band another feature
• Normalized light intensity another feature
• Total - 48 features
• Train the system with half the data
• 48 dimensional fingerprint

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Fingerprint Generation and Matching

• Match test fingerprint with trained database
• Use Nearest Neighbor algorithm for
  classification

Feature Extract
Location
Feature Extract
48 features
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Results

• SurroundSense offers consistent localization
• Database contains nearby shops in Duke campus
• Both sensors gt sound gt light

Pairwise Similarity
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• Symbolic localization can be augmented with
• phone accelerometers
• Additional benefits in activity recognition

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Hypothesis

• Movement partially indicative of location
• People sit in cafes
• Run in gyms
• Walk up/down aisles in grocery stores
• Time duration spent may depend on location
• Augment location accuracy with acc. signatures
• Enable activity recognition as well
• E.g., Advertize shoes to users running in the gym

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AAMPL Classification
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Evaluation

• Gathered acc. signatures from many restaurants
• Classified with AAMPL, compared with Google

Store Apple Wholefoods Journeys Solstice
Fast Food Chipotle Jimmy Johns
Restaurant Chais Verde Rockfish
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Evaluation

• AAMPL classified each location
• Compared corresponding location with Google

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Thoughts

• Main Idea is that the surrounding is a
  fingerprint.
• Effective for separating out nearby contexts.
• In reality,
• Spatially clustered shops are diverse by design
• Aids AAMPL and SurroundSense

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• Problem 3
• Location Privacy
• (CacheCloak)

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(3) Location Privacy

• Location information reveals context
• Thin line between utility and privacy
• Pseudonymns
• Effective only when infrequent querying from
  mobiles
• Else, spatio-temporal patterns enough to
  deanonymize

Leslie
Jack
John
Susan
Alex
Romits Office
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Location Privacy

• K-anonymity
• Convert location to a space-time bounding box
• Ensure K users in the box
• Location Service (LS) replies to the boxed region
• Issues
• Not real-time
• Poor quality of location
• Degrades in sparse regions

Bounding Box
You
K4
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Confusion Privacy

• Mixing or Cloaking users in space-time
• Can offer good QoL
• Issues
• Users need to be present in same space-time
  location
• Else, cloaking needs to be performed post priori

?
A
?
B
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Our Objective

• Real-time,
• high QoL,
• entropy guarantees,
• even in sparse populations

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Our Approach

• Exploit mobility prediction to deliberately
• create mix zones
• Accurate locations can be revealed
• that are all confusing to adversary

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CacheCloak

• Assume trusted privacy provider
• Reveal location to CacheCloak
• CacheCloak exposes anonymized location to LS

Loc. App1
Loc. App2
Loc. App3
Loc. App4
CacheCloak
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CacheCloak Design

• User A drives down path P1
• P1 is a sequence of locations
• CacheCloak has cached response for each location
• User A takes a new turn (no cached response)
• CacheCloak predicts mobility
• Deliberately intersects predicted path with other
  path (P2)
• Exposes predicted path to application
• Application must reply to queries for entire path
• Application/adversary confused
• New path emanates from both P1 and P2
• Not clear where the user came from

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Example
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Quantifying Privacy

• City converted into grid of small sqaures
  (pixels)
• Users are located at a pixel at a given time
• Each pixel associated with 8x8 matrix
• Element (i, j) probability that user enters i
  and exits j
• Probabilities diffuse
• At intersections
• Over time
• Privacy entropy

j
i
pixel
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Diffusion

• Probability of users presence diffuses
• Diffusion gradient computed based on history
• i.e., what fraction of users take right turn at
  this intersection
• When entropy needs to be increased
• Generate spurious branches

Time t1
Time t2
Time t3
Road Intersection
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CacheCloak Benefits

• Real-time
• Response ready when user arrives at predicted
  location
• High QoL
• Responses can be specific to location
• Of course, high overhead due to many responses
• Entropy guarantees
• Entropy increases at traffic intersections
• In low regions, desired entropy through false
  branching
• Sparse population
• Can be handled with dummy users

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Evaluation

• Trace based simulation
• VanetMobiSim US Census Bureau trace data
• Durham map with traffic lights, speed limits,
  etc.
• Vehicles follow Google map paths
• Performs collision avoidance

6km x 6km 10m x 10m pixel 1000 cars
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Results

• High average entropy
• Quite insensitive to user density (good for
  sparse regions)
• Minimum entropy reasonably high

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Results

• Length of predictions
• Remains reasonably short
• Overhead proportional to this length

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Issues and Limitations

• CacheCloak overhead
• Application replies to lots of queries
• However, overhead on wired infrastructure
• Caching reduces this overhead significantly
• CacheCloak assumes same, indistinguishable query
• If user asks different query at each road segment
• Overhead increases
• Adaptive branching dummy users
• Offer user-specified privacy guarantee

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Closing Thoughts

• Two nodes may intersect in space but not in time
• Mixing not possible
• Mobility prediction allows space-time
  intersections
• Enables better privacy

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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
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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
AAMPL Context aware localization CacheCloak
Location privacy via mobility prediction
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• Stay tuned for more at
• http//synrg.ee.duke.edu
• Thank You