accuracy characterization for metropolitan-scale wi-fi localization yu-chung cheng (ucsd, intel...
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Accuracy Characterization for Accuracy Characterization for Metropolitan-scale Wi-Fi LocalizationMetropolitan-scale Wi-Fi Localization
Yu-Chung Cheng (UCSD, Intel Yu-Chung Cheng (UCSD, Intel Research)Research)Yatin Chawathe (Intel Research)Yatin Chawathe (Intel Research)
Anthony LaMarca (Intel Research)Anthony LaMarca (Intel Research)
John Krumm (Microsoft Research)John Krumm (Microsoft Research)
slide2
IntroductionIntroduction
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
slide3
Riding the Wi-Fi waveRiding the Wi-Fi wave
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 What if we use war-driving
maps for positioning? Manhattan (Courtesy of Wigle.net)
slide4
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
slide5
MethodologyMethodology
Training phase– Collect AP beacons by “war
driving” with Wi-Fi card + GPS– Each scan records
A GPS coordinate List of Access Points
– Covers one neighborhood in 1 hr (~1 km2)
– Build radio map from AP traces
Positioning phase– Use radio map to position the user– Compare the estimated position w/
GPS
slide6
Downtown vs. Urban Residential vs. Downtown vs. Urban Residential vs. SuburbanSuburban
Downtown(Seattle)
Urban Residential(Ravenna)
Suburban(Kirkland)
slide7
EvaluationEvaluation
Choice of algorithms– Naïve, Fingerprint, Particle Filter
Environmental Factors– AP density: do more APs help?
– #APs/scan?
– AP churn: does AP turnover hurt?
– GPS noise: what if GPS is inaccurate?
Datasets– Scanning rate?
slide8
Compare Accuracy of Different AlgorithmsCompare Accuracy of Different Algorithms
Centroid– Estimate position as arithmetic mean of positions of all heard APs
Fingerprinting– User hears APs with some signal strength signature
– Match closest 3 signatures in the radio map
– RADAR: compare using absolute signal strengths [Bahl00]
– RANK: compare using relative ranking of signal strengths [Krumm03]
Particle Filters– Probabilistic approximation algorithm for Bayes filter
slide9
Baseline ResultsBaseline Results
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
slide10
Effect of APs per scanEffect of APs per scan
• More APs/scan lower median error• Rank does not work with 1 AP/scan
slide11
Effects of AP TurnoversEffects of AP Turnovers
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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
slide12
Effects of GPS noiseEffects of GPS noise
• Particle filter & Centroid are insensitive to GPS noise
slide13
Scanning densityScanning density
• 1 scan per 10 meters is good == 25 mph driving speed at 1 scan/sec• More war-drives do not help
slide14
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 acuracy– In urban area, simple (Centroid) algo. 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