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

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

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

slide15

Q & AQ & A

http://placelab.org