ACCURACY CHARACTERIZATION

FOR METROPOLITAN-SCALE WI-FI

LOCALIZATIONPresented by Jack Li

March 5, 2009

Acknowledgments

Authors Yu-Chung Cheng Yatin Chawathe Anthony LaMarca John Krumm

Some text and pictures taken from: http://sysnet.ucsd.edu/~ycheng/

papers/mobisys05placelab.ppt

Introduction

Many new applications need/can benefit from context-awareness Maps, directions on your phone Social applications (Twitter) Location-enhanced content (searching for nearby restaurants) Emergency services (911)

Problem Need to maximize coverage

Work wherever devices are taken Low calibration overhead

Must scale with coverage Low cost for user

Commodity devices (e.g. cell phones, music players)

Existing solutions

GPS high coverage and

accuracy (<10 m) Does not work indoors or

in urban canyons Not as prevalent

ActiveCampus Access point locations are

known RADAR

Median error of 2.94 meters

Many hours of installation

Proposed Solution

Wi-Fi is everywhere No new infrastructure Low cost for users Can be used indoors as

well as outdoors (RADAR)

“war driving” already builds AP maps

Two phases Training phase Positioning phase

Manhattan (Courtesy of Wigle.net)

Metrics for Positioning

Signal strength For a given location,

SSs are relatively stable Response rate

Collect all Wi-Fi scans that are at the same distance from an access point

Compute the fraction of times that this AP is heard in that collection of scans

Positioning Algorithms

Centroid Combine all the readings for a single access

point and take the arithmetic mean of the positions reported to estimate the AP’s geographic location

User is placed at the average position of all heard APs

AP1

AP2

AP3

USER

Positioning Algorithms

Fingerprinting Each scan is a unique “fingerprint” that

contains the GPS location and the associated APs for that location

Compute the k-nearest-neighbor in signal space

k = 4 provides good accuracy Match fingerprints that have at most p=2

different APs between the fingerprint in the radio map and observed scan

Positioning Algorithms

Ranking Used to account for variations in signal

strength that might be due to different devices/manufacturers

Compare a list of access points sorted by SS instead of absolute SS

(SSA, SSB, SSC) = (-20, -90, -40) converted into (RA, RB, RC) = (1, 3, 2)

Compare relative rankings using Spearman rank-order correlation coefficient [-1, 1]

Positioning Algorithms

Particle Filters Probabilistic approximation algorithm Location estimate of a user at time t using collection of

weighted particles Each particle is a distinct hypothesis about user’s current

location and each particle has an associated weight Sensor model

How likely it is that a given set of APs would be observed at a given location

Motion model Move the particles’ locations in a manner that approximates the

motion of the user SS and response rate sensor model built

Each scan, look up the response rate or the probability of seeing the measured SS based on the distance between the particle and the estimated AP location in the radio map

Methodology

Training phase Collect AP beacons by “war

driving” with Wi-Fi enabled laptop and attached GPS unit

Scans every second – each scan consists of list of APs and the associated GPS coordinate

1 km2 neighborhood covered in 1 hr

Build a radio map out of the AP traces – many different ways to build map

Positioning phase Use radio map to position the

user Compare the estimated

position with the actual GPS location

Downtown vs. Urban Residential vs. Suburban

Downtown(Seattle)

Urban Residential(Ravenna)

Suburban(Kirkland)

Results

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

Effect of #APs/scan and AP Turnovers

Effect of GPS noise and density of mapping data Centroid cancels out

some GPS noise Particle filter

techniques rely on empirical models built using the same radio map

25 mph driving speed with 1 scan/s is approximately 1 scan/10 meters

More “war drives” won’t help

Summary

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) algorithm 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

Issues

Some APs may be set to not broadcast – use passive scanning to detect network traffic

Did not perform any indoor traces – Place Lab cannot estimate room-level accuracy

Orinoco chipset used in all experiments – can report different SS values for the same AP at the same location

Q&A

Thanks for listening