The Horus WLAN Location Determination System

Moustafa Youssef and Ashok Agrawala

University of Maryland, College Park

June, 2005H

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2June, 2005 Horus © 2005, Moustafa Youssef

Location Determination Technologies

GPS Cellular-based Ultrasonic-based: Active Bat Infrared-based: Active Badge Computer vision: Easy Living Physical proximity: Smart Floor Not suitable for indoor

– Does not work– Require specialized hardware– Scalability

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3June, 2005 Horus © 2005, Moustafa Youssef

WLAN Location Determination

Triangulate user location

– Reference point

– Quantity proportional to distance

WLAN

– Access points

– Signal strength= f(distance)

Software based

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4June, 2005 Horus © 2005, Moustafa Youssef

Roadmap

Motivation

Location determination technologies

Introduction

Noisy wireless channel

Horus components

Performance evaluation

Conclusions and future work

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5June, 2005 Horus © 2005, Moustafa Youssef

WLAN Location Determination (Cont’d)

Signal strength= f(distance) Does not follow free space loss Use lookup table Radio map Radio Map: signal strength characteristics at selected

locations

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6June, 2005 Horus © 2005, Moustafa Youssef

WLAN Location Determination Taxonomy

WLAN Location Determination Systems

Ad-hoc Mode Infrastructure Mode

Cell of Origin Time of ArrivalSignal Strength

Model-based Radio-map Based

Radar Horus

Daedalus PinPoint

[Lundberg02]

Classification

Example

Deterministic ProbabilisticWheremops

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7June, 2005 Horus © 2005, Moustafa Youssef

WLAN Location Determination (Cont’d)

Offline phase– Build radio map– Radar system: average signal strength

Online phase– Get user location– Nearest location in signal strength space (Euclidian

distance)

[-53, -56]

[-50, -60]

[-58, -68]

5

13

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8June, 2005 Horus © 2005, Moustafa Youssef

Horus Goals

High accuracy

– Wider range of applications

Energy efficiency

– Energy constrained devices

Scalability

– Number of supported users

– Coverage area

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9June, 2005 Horus © 2005, Moustafa Youssef

Roadio-map

Motivation

Location determination technologies

Introduction

Noisy wireless channel

Horus components

Performance evaluation

Conclusions

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10June, 2005 Horus © 2005, Moustafa Youssef

Sampling Process

Active scanning– Send a probe

request– Receive a probe

responseChannel 2

Channel 1

...

1. Probe Request

2. Probe Response

3. Probe Request

4. Probe R

esponse

Channel n

2n-1

. P

robe R

equest

2n. P

robe R

esp

onse

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11June, 2005 Horus © 2005, Moustafa Youssef

Signal Strength Characteristics

Temporal variations

– One access point

– Multiple access points

Spatial variations

– Large scale

– Small scale

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12June, 2005 Horus © 2005, Moustafa Youssef

Temporal Variations:One Access Point

Environment changes

Using average only leads to loss of information

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Temporal Variations:Multiple Access Points

0

50

100

150

200

250

300

-95 -85 -75 -65 -55

Average Signal Strength (dBm)

Nu

mb

er

of

Sam

ple

s

Co

llecte

dReceiver Sensit ivity

Number of access points changes over time

Choose the strongest access points

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Temporal Variations:Correlation

Independence assumption is wrong

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Spatial Variations: Large-Scale

-65

-60

-55

-50

-45

-40

-35

-30

0 5 10 15 20 25 30 35 40 45 50 55

Distance (feet)

Sig

na

l S

tre

ng

th

(db

m)

Desirable !

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Spatial Variations: Small-Scale

Multipath effect

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Roadio-map

Motivation

Goals

Introduction

Noisy wireless channel

Horus components

Performance evaluation

Conclusions

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18June, 2005 Horus © 2005, Moustafa Youssef

Horus Components

Basic algorithm

Correlation handler

Continuous space estimator

Small-scale compensator

Locations clustering

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Offline phase

– Radio map: signal strength histograms

Online phase

– Bayesian based inference

Basic Algorithm

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Basic Algorithm:Example

(xi, yi)

(x, y)

-40 -60 -80

-40 -60 -80

[-53]

P(-53/L1)=0.55

P(-53/L2)=0.08

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21June, 2005 Horus © 2005, Moustafa Youssef

Using Multiple Samples

Need to average multiple samples to increase accuracy

Independence assumption is wrong

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Autoregressive model

– Estimate correlation degree

– Estimate distribution of the average of n correlated samples

Correlation Handler

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Correlation Handler:Var(A)/Var(s)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1a

Va

r(A

)/V

ar(

s)

0 1 2 3 4 5 6 7 8 9 10

Independence assumption underestimates true variance

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Enhance the discrete radio map space estimator

Two techniques

– Center of mass of the top ranked locations

– Time averaging window

Continuous Space Estimator

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25June, 2005 Horus © 2005, Moustafa Youssef

Small-scale Compensator

Perturbation Technique

Detect small-scale variations

– Using previous user location

Perturb signal strength vector

– (s1, s2, …, sn) (s1d1, s2d2, …, sndn)

– Typically, n=3-4

di is chosen relative to the received signalstrength

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Reduce computational requirements

Use access points that cover each location

Use the q strongest access points

Locations Clustering

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150

200

250

300

-95 -85 -75 -65 -55

Average Signal Strength (dBm)

Nu

mb

er

of

Sam

ple

s

Co

llecte

d

Receiver Sensit ivity

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Horus Components

Discrete-Space

Estimator

Continuous-Space

Estimator

Small-Scale

Compensator

Correlation

HandlerClustering

Correlation

Modeler

Radio Map

Builder

Radio

Map

and

clusters

Ho

rus S

yste

m C

om

po

ne

nts

Location API

Applications

Signal Strength Acquisition API

Estimated Location

Device Driver

(MAC, Signal Strength)(-50,-67,-80)

(-45,-63,-63)

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28June, 2005 Horus © 2005, Moustafa Youssef

Roadio-map

Motivation

Location Determination technologies

Introduction

Noisy wireless channel

Horus components

Performance evaluation

Conclusions and future work

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29June, 2005 Horus © 2005, Moustafa Youssef

Testbeds

A.V. William’s

– 4th floor, AVW

– 224 feet by 85.1 feet

– UMD net (Cisco APs)

– 21 APs (6 on avg.)

– 172 locations

– 5 feet apart

– Windows XP Prof.

FLA

– 3rd floor, 8400 Baltimore Ave

– 39 feet by 118 feet

– LinkSys/Cisco APs

– 6 APs (4 on avg.)

– 110 locations

– 7 feet apart

– Linux (kernel 2.5.7)

Orinoco/Compaq cards

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30June, 2005 Horus © 2005, Moustafa Youssef

Horus-Radar Comparison

0

500

1000

1500

2000

2500

3000

3500

4000

Horus RadarAv

g. N

um

. o

f O

pe

r. p

er L

oc

. E

st.

Median Avg Stdev Max

Horus (all components) 1.28 1.38 0.95 4

Horus (basic) 1.6 2.16 2.09 18.08

Radar 9.74 13.15 10.71 57.67

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Radar with Horus Techniques

Average distance error enhanced by more than 58%

Worst case error decreased by more than 76%

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32June, 2005 Horus © 2005, Moustafa Youssef

Roadio-map

Motivation

Location Determination technologies

Introduction

Noisy wireless channel

Horus components

Performance evaluation

Conclusions

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Conclusions

The Horus system achieves its goals

High accuracy– through different modules

Low computational requirements– through the use of clustering techniques

Scalability in terms of the coverage area– through the use of clustering techniques

Scalability in terms of the number of users– through the distributed implementation

Modules can be applied to other WLAN location determination systems

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Other Horus Related

Invention of the year award (UMD 2004) 3 Patents pending Licensed by Fujitsu Cited in

– New York Times– Washington Times

Software– Drivers: mwvlan, mwavelan, morinoco– MAPI– http://www.cs.umd.edu/~moustafa

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35June, 2005 Horus © 2005, Moustafa Youssef

For More Information

Overall system [MobiSys05]

Basic algorithm [Percom03]

Locations clustering [Percom03]

Small-scale compensator [WCNC03]

Optimality Analysis [CNDS04]

Correlation handler [InfoCom04]

Continuous space estimator [ICCCN04]

User profile [IJMS05]

Drivers/API’s

www.cs.umd.edu/users/moustafa

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36June, 2005 Horus © 2005, Moustafa Youssef

Thank You !

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