Practical Robust Localization over
Large-Scale Wireless Ethernet Networks
10th Annual International Conference on Mobile Computing
and Networking (MOBICOM)
September 28, 2004
Philadelphia, PA
Andreas Haeberlen
Eliot Flannery
Andrew Ladd
Algis Rudys
Dan Wallach
Lydia Kavraki
Rice University
Houston, TX
© 2004 Andreas Haeberlen, Rice University
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Motivation
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Goal: Locate a device in a building
Location-aware computing has many
interesting applications:
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Navigation
Asset tracking
Tourist/visitor guides
Advertising
Finding resources
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Visitor tracking
Content redirection
Robot navigation
Sensor networks
Intruder detection
The ideal localization system:
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© 2004 Andreas Haeberlen, Rice University
Cheap
Easy to deploy
Accurate
Robust
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Related Work
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Solutions with special hardware
 Good accuracy
 Expensive
 Hard to deploy
Ultrasound
beacons
Example: Cricket [Priyantha 2000]
© 2004 Andreas Haeberlen, Rice University
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Related Work
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Bayesian localization [Ladd 2002]
 Good accuracy
 Inexpensive hardware
But: Not practical!
 Needs many days
of training
 Does not work with
different hardware
 Accuracy varies during the day
© 2004 Andreas Haeberlen, Rice University
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Overview
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© 2004 Andreas Haeberlen, Rice University
Improvements over [Ladd 2002]:
 Drastic reduction in training time
 Adapts to different hardware
 Robust against untrained variations
Techniques used:
 Topological localization
 Simplified signal model
 Calibration
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Training
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Occurrences
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802.11 wireless
signal propagation
is complex
 Need training
Operator visits every
location, measures
signal strength
Result: A signal map
of the entire building
Observed signal strength
© 2004 Andreas Haeberlen, Rice University
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Markov Localization
Signal map
P(o j | si )
Observed
RSSI
Location
estimate
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o
i
Bayes' formula
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 i 1 
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P(o j | si )   i
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New location
 estimate
 i 1
© 2004 Andreas Haeberlen, Rice University
To localize
1. Initialize vector of
location estimates
2. Perform a base
station scan
3. Update estimate
using Bayes'
formula
4. Repeat steps 2-3
until estimates
converge
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Topological regions
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Occupancy grid
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Regions
© 2004 Andreas Haeberlen, Rice University
Many applications do not
need 1-2 meter precision
Can trade metric resolution
for lower training time
Localize to regions
 Offices
 Hallway segments
 Parts of larger rooms
Reduces training effort by
an order of magnitude
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Gaussian signal model
Occurrences
Gap
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Minor mode
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Observed signal strength
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Occurrences
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Overtraining
Undertraining
Use Gaussian as an
approximation!
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Observed signal strength
© 2004 Andreas Haeberlen, Rice University
Previous methods
keep a histogram
of signal strengths
Problems
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More robust
Saves memory
Needs less training
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Experiment: Duncan Hall
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Duncan Hall: >200 offices, classrooms, seminar rooms
Total area: 158 x 75 meters
© 2004 Andreas Haeberlen, Rice University
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Duncan Hall Architecture
Large open spaces
(low signal variation)
Clerestory ceiling
(reflections)
© 2004 Andreas Haeberlen, Rice University
Metal air ducts
(distortions)
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Experiment: Duncan Hall
Data collection:
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Manually created 510 cells, ~3x5m each
Collected 100 BS scans/cell (51249 total)
28 man-hours were sufficient!
Experiments:
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© 2004 Andreas Haeberlen, Rice University
Partition data set
 Training data
 Testing data
51249 scans
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Results: Static localization
Accuracy for cell:
70-80%
Base stations
80-90%
90-95%
>95%
worst case
(localizes to
adjacent cells)
Result: Excellent accuracy over the entire building
© 2004 Andreas Haeberlen, Rice University
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Results: Static localization II
For 95% accuracy:
Histogram: 84 scans
Gaussian: 30 scans
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Experiment: Use only N scans/cell for training
Result: Gaussian needs a lot less training data
This is in addition to gains from topology model
© 2004 Andreas Haeberlen, Rice University
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Problem: Untrained variations
Source: [Tao 2003]
Signal Strength
140
100
0.12
60
0.06
20
3am
1.
2.
9am
3pm
9pm
3am
Probability of registering
signal strength
0.18
0.00
Differences in hardware, software, or antenna
Observed signal strength changes over time
© 2004 Andreas Haeberlen, Rice University
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Signal strength (new card)
Calibration: New Hardware
256
i2=m·i1+c
192
128
64
0
0
64
128
192
256
Signal strength (reference card)
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© 2004 Andreas Haeberlen, Rice University
Approximate relationship between 'old' and
'new' values by a linear function
Invert function, apply it to each observation
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Signal strength (11am)
Calibration: Time-of-day
256
i2=m·i1+c
192
Parameters
128
64
0
0
64
128
192
256
Signal strength (nighttime)
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© 2004 Andreas Haeberlen, Rice University
Linear approximation works for time-of-day
variations, too!
Learn parameters using calibration
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Mobility: Markov chains
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5% 5%
5%
60%
90%
5%
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5% 5%
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© 2004 Andreas Haeberlen, Rice University
Goal: Track location
while user is moving
Problem: Markov
localization tends to
'lag' for mobile agent
Need a motion model
for the user
Use markov chain to
model possible cellto-cell transitions
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"It doesn't work any more!"
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Base stations were upgraded to 802.11a/b/g
 New IOS software
2.4 GHz radio
module
 New radio module
What we did:
 Configured new BSSIDs
 Ran calibration once
System works, delivers good accuracy!
© 2004 Andreas Haeberlen, Rice University
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Results: Mobility
- Movie -
Experiment on 09/23/04
(after 802.11a/b/g upgrade)
© 2004 Andreas Haeberlen, Rice University
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Conclusions
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Topological localization delivers good accuracy
with a reasonable training effort
Gaussian sensor model is more robust and
requires less training time than histogrambased model
Training data can be adapted for use with
different hardware and under different
conditions
System is deployed in a large office building
and in practical use
© 2004 Andreas Haeberlen, Rice University
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