accuracy characterization for metropolitan-scale wi-fi localization ying wang, xia li ying wang, xia...
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Accuracy Characterization for Accuracy Characterization for Metropolitan-scale Wi-Fi LocalizationMetropolitan-scale Wi-Fi Localization
Ying Wang, Xia LiYing Wang, Xia Li
Introduction-Introduction-What does the paper doWhat does the paper do
Outdoor Location mechanism based on Wi-Fi
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-Introduction-Why We Need LocationWhy We Need Location
Context-aware applications are prevalent– Maps– Location-enhanced content– Social applications– Emergency services (E911)
A key enabler: location systems– Must have high coverage
Work wherever we take the devices
– Low calibration overhead Scale with the coverage
– Low cost Commodity devices
Introduction-Introduction-Why not just use GPS?Why not just use GPS?
High coverage and accuracy (<10m)
But, does not work indoors or in urban canyons
GPS devices are not nearly as prevalent as Wi-Fi
Introduction-Introduction-Why Wi-FiWhy Wi-Fi
Wi-Fi is everywhere now– No new infrastructure– Low cost– APs broadcast beacons– “War drivers” already build AP
maps Calibrated using GPS Constantly updated
Position using Wi-Fi– Indoor Wi-Fi positioning gives 2-
3m accuracy– But requires high calibration
overhead: 10+ hours per building
Manhattan (Courtesy of Wigle.net)
MethodologyMethodology
1. Training phase (war driving)
Position 1Position 2 Position 3
GPS
Wifi card
(x1, y1)(x3, y3)
(x2, y2)
• A GPS coordinate• List of Access Points
MethodologyMethodology
2. Positioning phase
(x1, y1)
(x3, y3)
(x2, y2)
Position 1
Position 2
Position 3
• Use radio map to position the user
(x’, y’)
A
B C
MethodologyMethodology
Problem: How to make position estimation?
(x’, y’)
(x3, y3)
Answer: By using positioning algorithms
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
1.Centroid Algorithm• Basic Centroid
• Weighted Centroid
2. Fingerprinting Algorithm• Radar Fingerprinting
• Ranking Fingerprinting
3. Particle Filters
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
1. Centroid Algorithm
Basic Centroid
AP1(x1,y1)
AP3(x3,y3)
AP2(x2,y2)
3'
3'
321
321
yyyy
xxxx
(x’, y’)
Estimated
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
1. Centroid Algorithm
Weighted CentroidAP1 (x1,y1)
AP3 (x3,y3)
AP2 (x2,y2)
3'
3'
332211
332211
ywywywy
xwxwxwx
ss1ss2
ss3
(x’, y’)
132
132
www
ssssss
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
2. Fingerprinting Algorithm
What is Fingerprinting?
(x1, y1)
ss
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
2. Fingerprinting Algorithm
Radar Fingerprinting
A
C
B ssA ssB
ssC
ss’A ss’B
ss’C 222 )'()'(' CCBBAA SSSSSSSSSSSS )(
choose “4” nearest GPS coordinates
GPS coordinate
Access Points
New user
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
2. Fingerprinting Algorithm
Ranking Fingerprinting
All hardware will not give same signal strength Instead of comparing signal strength directly, this method compares the rank of signal strength
is spearman coefficient. Higher -> more similar rankings
SS = (-20, -90, -40) R = (1,3,2)
sr sr
222 )'()'(' CCBBAA SSSSSSSSSSSS )(
Methodology-Methodology-Positioning AlgorithmPositioning Algorithm
3. Particle Filters
Key point of Particle Filter: Fusion
2211 estimationestimationfinal pwpwp
Sensor Model Motion Model
Note: The actual fusion calculation is more complicated, not this linear equation
Results-Results-HistogramHistogram
0
10
20
30
40
50
60
70
Downtown UrbanResidential
Suburban
Me
dia
n E
rro
r (m
ete
rs) Centroid (Basic)
Fingerprint (Radar)
Fingerprint (Rank)
Particle Filter
• Algorithms matter less (except rank)• AP density (horizontal/vertical) matters
EvaluationEvaluation
Choice of algorithms– Naïve, Fingerprint, Particle Filter
Environmental Factors– AP density: do more APs help?
– AP churn: does AP turnover hurt?
– GPS noise: what if GPS is inaccurate?
– Scanning rate?
Effect of APs per scanEffect of APs per scan
• More APs/scan lower median error• Rank does not work with 1 AP/scan
Effects of AP TurnoversEffects of AP Turnovers
0
20
40
60
80
100
0% 20% 40% 60% 80% 100%AP Turnovers
Med
ian
erro
r (m
eter
s)
centroid
particle filter
radar
rank
• Minimal effect on accuracy even with 60% AP turnover
Scanning densityScanning density
• 1 scan per 10 meters is good == 25 mph driving speed at 1 scan/sec• More war-drives do not help
SummarySummary
Wi-Fi-based location with low calibration overhead– 1 city neighborhood in 1 hour
Positioning accuracy depends mostly on AP density– 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
– Low calibration overhead
Noise in GPS only affects fingerprint algorithms