September 16,2003 APIT @ MobiCom'03 University of Virginia 1 Range-Free Localization Schemes in...

September 16,2003 APIT @ MobiCom'03 University of Virginia

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Range-Free Localization Schemes in Large Scale Sensor Networks

Range-Free Localization Schemes in Large Scale Sensor Networks

Tian He

Chengdu Huang

Brian. M. Blum

John A. Stankovic

Tarek F. Abdelzaher

Department of Computer Science, University of Virginia

http://www.acm.org/sigmobile/

http://www.acm.org/


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OutlineOutline

• Problem Statement

• State of the Art

• Motivation & Contribution

• A.P.I.T. Algorithm Details

• Evaluation

• Conclusion


http://www.acm.org/


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Problem StatementProblem Statement

• Localization Problem: – How nodes discover their geographic positions in 2D or 3D space?

• Target Systems:– Static large scale sensor networks or one with a low mobility

• Goal:– An affordable solution suitable for large-scale deployment with a

precision sufficient for many sensor applications.


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State of the Art (1)State of the Art (1)

• Range-based Fine-grained localizations – TOA (Time of Arrival ): GPS– TDOA (Time Difference of Arrival): MIT Cricket &

UCLA AHLos – AOA (Angle of Arrive ): Aviation System and Rutgers

APS– RSSI (Receive Signal Strength Indicator) : Microsoft

RADAR and UW SpotOn

Required Expensive hardware

Limited working range ( Dense anchor requirement)

Log-normal model doesn’t hold well in practice [D. Ganesan]


http://www.acm.org/


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State of the Art (2)State of the Art (2)

• Range-Free Coarse-grained localization– USC/ISI Centroid localization

– Rutgers DV-Hop Localization

– MIT Amorphous Localization

– AT&T Active Badge

Simple hardware/ Less accuracy


http://www.acm.org/


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MotivationMotivation

• High precision in sensor network localization is overkill for a lot of applications.

• Large scale deployment require cost-effective solutions.

Routing Delivery Ratio Entity Tracking Time

Under different localization Error ( % Radio Range)

0%

20%

40%

60%

80%

100%

6 8 10 12 14 16 18 20

Node Density

No Error

0.2R

0.4R

0.6R

0.8R

1.0R

100%

110%

120%

130%

140%

5 6 7 8 9 10

Pursuer speed (units/sec)

No

rmal

ized

tra

ckin

g t

ime 0.4R

0.6R

0.8R

1.0R


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ContributionsContributions

• A novel range-free algorithm with enhanced performance under irregular radio patterns and random node placement with a much smaller overhead than flooding based solutions

• The first to provide a realistic and detailed quantitative comparison of existing range-free algorithms.

• First investigation into the effect of localization accuracy on application performance


http://www.acm.org/


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Overview of APIT AlgorithmOverview of APIT Algorithm

• APIT employs a novel area-based approach. Anchors divide terrain into triangular regions

• A node’s presence inside or outside of these triangular regions allows a node to narrow the area in which it can potentially reside.

• The method to do so is called Approximate Point In Triangle Test (APIT). Out

IN

IN


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IN

IN

APIT Main AlgorithmAPIT Main Algorithm

Pseudo Code:

Receive beacons (Xi,Yi) from N anchors

N anchors form triangles.

For ( each triangle Ti Є ){

InsideSet Point-In-Triangle-Test (Ti)

}

Position = COG ( ∩Ti InsideSet);

For each node

• Anchor Beaconing

• Individual APIT Test

• Triangle Aggregation

• Center of Gravity Estim.

3

N

3

N

OUT


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Point-In-Triangle-Test Point-In-Triangle-Test

• For three anchors with known positions: A(ax,ay), B(bx,by), C(cx,cy), determine whether a point M with an unknown

position is inside triangle ∆ABC or not.

B(bx,by)C(cx,cy),

A(ax,ay)

M


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Perfect P.I.T TheoryPerfect P.I.T Theory

• If there exists a direction in which M is departure from points A, B, and C simultaneously, then M is outside of ∆ABC. Otherwise, M is inside ∆ABC.

• Require approximation for practical use– Nodes can’t move, how to recognize direction of departure

– Exhaustive test on all directions is impractical

M

A

CB

MA

CB

Inside Case Outside Case


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Departure TestDeparture Test

Recognize directions of departure via neighbor exchange1. Receiving Power Comparison ( the solution we adopt)

2. Smoothed Hop Distance Comparison ( Nagpal 1999 MIT)

Experimental Result from Berkeley Experiment Result from UVA

M

NA

Anchor Receiving nodes

300

350

400

450

500

550

600

1 5 9 13 17 21 25 29 33 37Beacon Sequence Number

Sign

al S

tren

gth

(mv) 1 Foot

5 Feet

10 Feet

15 Feet


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A.P.I.T. TestA.P.I.T. Test

Approximation: Test only directions towards neighbors – Error in individual test exists , however is relatively small and can be masked

by APIT aggregation.

A

C

1

23

4

M

B

A

CB

A. Inside Case B. OutSide Case

1

23

4

M

APIT(A,B,C,M) = IN APIT(A,B,C,M) = OUT


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APIT AggregationAPIT Aggregation

• Aggregation provides a good accuracy, even results by individual tests are coarse and error prone.

With a density 10 nodes/circle, Average 92% A.P.I.T Test is correctAverage 8% A.P.I.T Test is wrong

Localization Simulation example

Grid-Based Aggregation

High Possibility area

Low possibility area

-1-1-10011100

-1-1-10122200

0-1-10112211

00-10112210

0001111100

0001110100

0001000000

-1-1-10000

-1-10100

0-1011

00-1010

00011100

00011000

0001100000


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Evaluation (1) Evaluation (1) • Comparison with state-of-the art solutions

– USC/ISI Centroid localization by N.Bulusu and J. Heidemann 2000

– Rutgers DV-Hop Localization by D.Niculescu and B. Nath 2003

– MIT Amorphous Localization by R. Nagpal 2003

Centroid DV-Hop (online)/ Amorphous (offline)


http://www.acm.org/


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Evaluation (2)Evaluation (2)

• Radio Model: Continuous Radio Variation Model.– Degree of Irregularity (DOI ) is defined as maximum radio range variation

per unit degree change in the direction of radio propagation

DOI =0 DOI = 0.05 DOI = 0.2

α


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Simulation SetupSimulation Setup

• Setup – 1000 by 1000m area

– 2000 ~ 4000 nodes ( random or uniform placement )

– 10 to 30 anchors ( random or uniform placement )

– Node density: 6 ~ 20 node/ radio range

– Anchor percentage 0.5~2%

– 90% confidence intervals are within in 5~10% of the mean

• Metrics– Localization Estimation Error ( normalized to units of radio

range)

– Communication Overhead in terms of #message


http://www.acm.org/


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Error Reduction by Increasing #AnchorsError Reduction by Increasing #Anchors

AH=10~28,ND = 8, ANR = 10, DOI = 0

Placement = Uniform Placement = Random

0

0.5

1

1.5

2

2.5

10 14 18 22 26

Anchor Heard

Centroid AmorphousDV-Hop A.P.I.T

P.I.T.

0

0.5

1

1.5

2

2.5

10 14 18 22 26

Anchor Heard

Centroid Amorphous

DV-Hop A.P.I.TP.I.T.


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0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

6 10 14 18 22

Node Density

Centroid Amorphous

DV-Hop A.P.I.T

Error Reduction by Increasing Node DensityError Reduction by Increasing Node Density

AH=16, Uniform, AP = 0.6%~2%, ANR = 10

DOI=0.1 DOI=0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

6 10 14 18 22

Node Density

Centroid Amorphous

DV-Hop A.P.I.T


http://www.acm.org/


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Error Under Varying DOIError Under Varying DOI

ND = 8, AH=16, AP = 2%, ANR = 10

Placement = Uniform Placement = Random

0

0.5

1

1.5

2

2.5

3

3.5

0 0.1 0.2 0.3 0.4 0.5 0.6

Degree of irregularity

Centroid

Amorphous

DV-Hop

A.P.I.T

0

0.5

1

1.5

2

2.5

3

3.5

0 0.1 0.2 0.3 0.4 0.5 0.6

Degree of irregularity

Centroid

Amorphous

DV-Hop

A.P.I.T


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Communication OverheadCommunication Overhead

• Centroid and APIT– Long beacons

• DV-Hop and Amorphous– Short beacons

• Assume: 1 long beacon = Range2 short beacons = 100 short beacons

• APIT > Centroid– Neighborhood information

exchange

• DV-Hop > Amorphous– Online HopSize estimation ANR=10, AH = 16, DOI = 0.1, Uniform

0

5000

10000

15000

20000

25000

30000

6 11 15 18 22

Node Density

Centroid

AmorphousDV-Hop

A.P.I.T


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Performance SummaryPerformance Summary

Centroid DV-Hop Amorphous APITAccuracy Fair Good Good Good

Node Density >0 >8 >8 >6

Anchor >10 >8 >8 >10

ANR >0 >0 >0 >3

DOI Good Good Fair Good

GPSError Good Good Fair Good

Overhead Smallest Largest Large Small


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Hermes Project @ UVAHermes Project @ UVA

Hermes Network Architecture

Application

Embedded Device

Environmental Monitor Military Suvilliance

Emergency Response

Biomedicine

Smart spaceLearning environment

Entity-Aware Transport

Local-Addressed Soft Real-time Routing

Robust and Self-stabilized MAC

Localization

Application Independent Data Aggregation

Service API

Differentiated Packet Scheduling

Power and Coverage management

NEST Demo

EnviroTrack

Real-Time Routing

QoS Scheduling

Data Aggregation

Lazy Binding MAC

Sensing Coverage

APIT Localization

Mote Test Bed


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ConclusionsConclusions

• Range-free schemes are cost-effective solutions for large scale sensor networks.

• Through a robust aggregation, APIT performs best with irregular radio patterns and random node placements

• APIT performs well with a low communication overhead( e.g. 2500 instead of 25,000 msgs)


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Questions?Questions?

Thanks


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Error Case Error Case

Since the number of neighbors is limited, an exhaustive test on every direction is impossible.

– InToOut Error can happen when M is near the edge of the triangle

– OutToIn Error can happen with irregular placement of neighbors

1

2

4

M

A

C

1

2

4

B

A

CB

A. InToOut Error B. OutToIn Error

3

M

PIT = IN while APIT = OUT PIT = OUT while APIT = IN


http://www.acm.org/


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Empirical Study on APIT ApproximationEmpirical Study on APIT Approximation

• Percentage of error due to APIT approximation is relatively small (e.g. 14% in the worst case, 8% when density is 10)

• More important, Errors can be masked by APIT aggregation.

APIT Error under Varying Node Densities

0%

2%

4%

6%

8%

10%

12%

14%

16%

6 8 10 12 14 16 18 20 22 24Node Density Per Radio Range

OutToInErrorPercentage

InToOutErrorPercentage


http://www.acm.org/

September 16,2003 APIT @ MobiCom'03 University of Virginia 1 Range-Free Localization Schemes in...

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