© 2004 andreas haeberlen, rice university 1 practical robust localization over large-scale wireless...

© 2004 Andreas Haeberlen, Rice University1

Practical Robust Localization over Large-Scale Wireless Ethernet Networks

Andreas Haeberlen

Eliot Flannery

Andrew Ladd

Algis Rudys

Dan Wallach

Lydia Kavraki

Rice UniversityHouston, TX

10th Annual International Conference on Mobile Computingand Networking (MOBICOM)

September 28, 2004Philadelphia, PA

2© 2004 Andreas Haeberlen, Rice University

Motivation

Location-aware computing has many interesting applications:

Navigation Asset tracking Tourist/visitor guides Advertising Finding resources

Visitor tracking Content redirection Robot navigation Sensor networks Intruder detection

Goal: Locate a device in a building

The ideal localization system:CheapEasy to deployAccurateRobust


Related Work

Solutions with special hardware Good accuracy Expensive Hard to deploy

Example: Cricket [Priyantha 2000]

Ultrasoundbeacons


Related Work

Bayesian localization [Ladd 2002] Good accuracy Inexpensive hardware

But: Not practical! Needs many days

of training Does not work with

different hardware Accuracy varies during the day


Overview

Improvements over [Ladd 2002]: Drastic reduction in training time Adapts to different hardware Robust against untrained variations

Techniques used: Topological localization Simplified signal model Calibration


Training 802.11 wireless

signal propagation is complex Need training

Operator visits every location, measures signal strength

Result: A signal map of the entire building

Observed signal strength

Occ

urr

ence

s


Markov Localization

To localize1. Initialize vector of

location estimates

2. Perform a base station scan

3. Update estimate using Bayes' formula

4. Repeat steps 2-3 until estimates converge

Signal map)|( ij soP

Locationestimate

iObserved

RSSI

o

iiji

soP

)|(1

Bayes' formula

New location estimate

1i


Topological regions Many applications do not

need 1-2 meter precision Can trade metric

resolution for lower training time

Localize to regions Offices Hallway segments Parts of larger rooms

Reduces training effort by an order of magnitude

Occupancy grid

Regions


Gaussian signal model Previous methods

keep a histogram of signal strengths

Problems Overtraining Undertraining

Use Gaussian as an approximation! More robust Saves memory Needs less training


Occ

urr

en

ces


Occ

urr

en

ces

Minor mode

Gap


Experiment: Duncan Hall

Duncan Hall: >200 offices, classrooms, seminar rooms Total area: 158 x 75 meters


Duncan Hall ArchitectureLarge open spaces

(low signal variation)

Clerestory ceiling(reflections)

Metal air ducts(distortions)


Experiment: Duncan Hall

Manually created 510 cells, ~3x5m each Collected 100 BS scans/cell (51249 total) 28 man-hours were sufficient!

Data collection:

Experiments: Partition data set

Training data Testing data

51249 scans


Results: Static localization

Result: Excellent accuracy over the entire building

Accuracy for cell:70-80% 80-90% 90-95% >95%

Base stationsworst case(localizes to

adjacent cells)


Results: Static localization II

Experiment: Use only N scans/cell for training Result: Gaussian needs a lot less training data This is in addition to gains from topology model

For 95% accuracy: Histogram: 84 scans Gaussian: 30 scans


Problem: Untrained variations

1. Differences in hardware, software, or antenna2. Observed signal strength changes over time

Sig

nal S

tren

gth

3am 3am9am 3pm 9pm

140

100

60

20

Source: [Tao 2003]

Pro

bab

ility

of

reg

iste

rin

gsi

gn

al st

ren

gth

0.18

0.00

0.12

0.06


Calibration: New Hardware

Approximate relationship between 'old' and 'new' values by a linear function

Invert function, apply it to each observation

Signal strength (reference card)

0 25612864 192

256

192

128

64

0

Sig

nal st

ren

gth

(n

ew

card

)

i2=m·i1+c


Calibration: Time-of-day

Linear approximation works for time-of-day variations, too!

Learn parameters using calibration

Signal strength (nighttime)

0 25612864 192

256

192

128

64

0Sig

nal st

ren

gth

(1

1am

)

i2=m·i1+c

Parameters


Mobility: Markov chains Goal: Track location

while user is moving Problem: Markov

localization tends to 'lag' for mobile agent

Need a motion model for the user

Use markov chain to model possible cell-to-cell transitions

90%

5% 5%

5% 5%

5%5%

60%


"It doesn't work any more!"

Base stations were upgraded to 802.11a/b/g New IOS software New radio module

What we did: Configured new BSSIDs Ran calibration once

System works, delivers good accuracy!

2.4 GHz radiomodule


Results: Mobility

- Movie -

Experiment on 09/23/04(after 802.11a/b/g upgrade)


Conclusions Topological localization delivers good

accuracy with a reasonable training effort Gaussian sensor model is more robust

and requires less training time than histogram-based model

Training data can be adapted for use with different hardware and under different conditions

System is deployed in a large office building and in practical use

© 2004 andreas haeberlen, rice university 1 practical robust localization over large-scale wireless...

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