Transcript Accuracy Characterization for Metropolitan-scale Wi

Accuracy Characterization for
Metropolitan-scale Wi-Fi Localization
Ying Wang, Xia Li
Outline
 Introduction
 Methodology
 Results
 Evaluation
 Summary
Introduction-What does the paper do

Outdoor Location mechanism based on
Wi-Fi
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Explore the question of how accurately a
user's device can estimate its location
using existing hardware and
infrastructure and with minimal
calibration overhead
slide3
Introduction-Why We Need Location
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Context-aware applications are prevalent
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Maps
Location-enhanced content
Social applications
Emergency services (E911)
A key enabler: location systems
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Must have high coverage
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Low calibration overhead
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Work wherever we take the devices
Scale with the coverage
Low cost
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Commodity devices
Introduction-Why not just use GPS?
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High coverage and accuracy (<10m)
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But, does not work indoors or in urban
canyons
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GPS devices are not nearly as prevalent
as Wi-Fi
Introduction-Why Wi-Fi
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Wi-Fi is everywhere now
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No new infrastructure
Low cost
APs broadcast beacons
“War drivers” already build AP
maps
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Calibrated using GPS
Constantly updated
Position using Wi-Fi
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Indoor Wi-Fi positioning gives 23m accuracy
But requires high calibration
overhead: 10+ hours per building
Manhattan (Courtesy of Wigle.net)
Methodology
1. Training phase (war driving)
• A GPS coordinate
• List of Access Points
GPS
Wifi card
(x1, y1)
(x2, y2)
(x3, y3)
Position 1
Position 2
Position 3
Methodology
2. Positioning phase
• Use radio map to position the user
(x2, y2)
A
(x1, y1)
(x’, y’)
Position 2
Position 1
(x3, y3)
B
C
Position 3
Methodology
(x’, y’)
(x3, y3)
Problem: How to make position estimation?
Answer: By using positioning algorithms
Methodology-Positioning Algorithm
1.Centroid Algorithm
• Basic Centroid
• Weighted Centroid
2. Fingerprinting Algorithm
• Radar Fingerprinting
• Ranking Fingerprinting
3. Particle Filters
Methodology-Positioning Algorithm
1. Centroid Algorithm

Basic Centroid
AP1(x1,y1)
AP2(x2,y2)
(x’, y’)
Estimated
AP3(x3,y3)
x1  x2  x3

 x' 
3

 y '  y1  y2  y3

3
Methodology-Positioning Algorithm
1. Centroid Algorithm

Weighted Centroid
AP1 (x1,y1)
AP2 (x2,y2)
ss1
ss2
(x’, y’)
ss3
AP3 (x3,y3)
w1  x1  w2  x2  w3  x3

 x' 
3

 y '  w1  y1  w2  y2  w3  y3

3
ss2  ss3  ss1
w2  w3  w1
Methodology-Positioning Algorithm
2. Fingerprinting Algorithm

What is Fingerprinting?
(x1, y1)
ss
Methodology-Positioning Algorithm
2. Fingerprinting Algorithm

A
Radar Fingerprinting
ssA
ssB
GPS coordinate
Access Points
ss’A
ssC
B
ss’B
ss’C
New user
2
（SS A  SS ' A ）
 ( SS B  SS 'B ) 2  ( SS C  SS 'C ) 2
C
choose “4” nearest GPS coordinates
Methodology-Positioning Algorithm
2. Fingerprinting Algorithm

Ranking Fingerprinting
 All hardware will not give same signal strength
2signal strength2 directly, this method
2
 Instead of
comparing
（SS

SS
'
）

(
SS

SS
'
)

(
SS

SS
'
)
A
A
B
B
C
C
compares the rank of signal strength
SS = (-20, -90, -40)

R = (1,3,2)
 rs is spearman coefficient. Higher rs -> more similar
rankings
Methodology-Positioning Algorithm
3. Particle Filters
Key point of Particle Filter: Fusion
p final  w1  pestimation1  w2  pestimation2
Sensor Model
Motion Model
Note: The actual fusion calculation is more
complicated, not this linear equation
Results-AP Density
Downtown
(Seattle)
Urban Residential
(Ravenna)
Suburban
(Kirkland)
Results-Table
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Median error in meters for all of algorithms
across the three areas
Results-Histogram
70
Median Error (meters)
60
Centroid (Basic)
50
Fingerprint (Radar)
40
Fingerprint (Rank)
30
Particle Filter
20
10
0
Downtown
Urban
Residential
Suburban
• Algorithms matter less (except rank)
• AP density (horizontal/vertical) matters
Evaluation
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Choice of algorithms
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Naïve, Fingerprint, Particle Filter
Environmental Factors
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AP density: do more APs help?
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AP churn: does AP turnover hurt?
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GPS noise: what if GPS is inaccurate?
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Scanning rate?
Effect of APs per scan
• More APs/scan  lower median error
• Rank does not work with 1 AP/scan
Effects of AP Turnovers
Median error (meters)
100
80
centroid
60
particle filter
40
radar
rank
20
0
0%
20%
40%
60%
AP Turnovers
80%
• Minimal effect on accuracy even with 60% AP
turnover
100%
Effects of GPS noise
• Particle filter & Centroid are insensitive to GPS noise
Scanning density
• 1 scan per 10 meters is good == 25 mph driving speed at 1 scan/sec
• More war-drives do not help
Summary
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Wi-Fi-based location with low calibration overhead
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Positioning accuracy depends mostly on AP density
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Urban 13~20m, Suburban ~40m
Dense AP records get better accuracy
In urban area, simple (Centroid) yields same accuracy as other
complex ones
AP turnovers & low training data density do not degrade
accuracy significantly
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1 city neighborhood in 1 hour
Low calibration overhead
Noise in GPS only affects fingerprint algorithms
Q&A
Any Questions?
*The slides were edited based on the original ppt from Yu-Chung Cheng