Localization Murat Demirbas SUNY Buffalo. 2 Localization Localization of a node refers to the problem of identifying its spatial co-ordinates in some

Localization

Murat Demirbas

SUNY Buffalo

2

Localization

• Localization of a node refers to the problem of identifying its spatial co-ordinates in some co-ordinate system

How do nodes discover their geographic positions in 2D or 3D space?

• Model: static wireless sensor networks

3

Location Matters

• Sensor Net Applications

Environment monitoring Event tracking

Smart environment

• Geographic routing protocols

GeoCast, GPSR, LAR, GAF, GEAR

4

Outline

• Range-based localization

GPS Cricket APS RADAR

• Range-free localization

Centroid DV-HOP APIT

5

Range-based localization

Distances between nodes to nodes/anchors measured wirelessly

• TOA (Time of Arrival )

GPS

• TDOA (Time Difference of Arrival)

Cricket

• AOA (Angle of Arrive )

APS

• RSSI (Receive Signal Strength Indicator)

RADAR

6

Outline





7

Time of arrival (TOA)

B. H. Wellenhoff, H. Lichtenegger and J. Collins, Global Positioning System: Theory and Practice. Fourth Edition, Springer Verlag, 1997

• Example: GPS

• Uses a satellite constellation of at least 24 satellites with atomic clocks

• Satellites broadcast precise time

• Estimate distance to satellite using signal TOA

• Trilateration

8

Outline





9

Sound based ToF approach

Because the speed of sound is much slower (approximately 331.4m/s) than radio, it is easier to be applied in sensor network.

Some hurdles are:• Line of sight path must exist between sender and receiver.• Mono-direction.• Short range.

10

Cricket

• Intended for indoors use where GPS don't work

• It can provide distance ranging and positioning precision of between 1 and 3 cm

• Active beacons and passive listeners

11

Outline





12

Angle of arrival (AOA)

Dragos Niculescu and Badri Nath. Ad Hoc Positioning System (APS) Using AoA, IEEE InfoCom 2003

• Idea: Use antenna array to measure direction of neighbors

• Special landmarks have compass + GPS, broadcast location and bearing

• Flood beacons, update bearing along the way

• Once bearing of three landmarks is known,calculate position

"Medusa" mote

13

Outline





14

RADAR

• Bahl: MS research

• Offline calibration:

Tabulate <location, RSSI> to construct radio map

• Real-time location & tracking:

Extract RSSI from base station beacons Find table entry best matching the measurement

15

Problems with RSSI

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• Sensors have wireless transceivers anyway, so why not just use the RSSI to estimate distances?

• Problem: Irregular signal propagation characteristics (fading, interference, multi-path etc.)

16

Outline





17

Range-free localization

Range-based localization:

Required Expensive hardware

Limited working range ( Dense anchor requirement)

Range-free localization:

Simple hardware Less accuracy

18

Outline





19

Range-free: Centroid

Nirupama Bulusu, John Heidemann and Deborah Estrin. Density Adaptive Beacon Placement, Proceedings of the 21st IEEE ICDCS, 2001

• Idea: Do not use any ranging at all, simply deploy enough beacons

• Anchors periodically broadcast their location

• Localization:

Listen for beacons

Average locations of all anchors in range

Result is location estimate

• Good anchor placement is crucial!

Anchors

20

Outline





21

Hop-Count Techniques

DV-HOP [Niculescu & Nath, 2003]Amorphous [Nagpal et. al, 2003]

Works well with a few, well-located seeds and regular, static node distribution. Works poorly if nodes move or are unevenly distributed.

r

1

1

2

23

3

33

4

4

4

44

5

5

6

7

8

22

Outline





23

Overview of APIT

• 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).

24

Algorithm

• Anchor Beaconing

• Individual APIT Test

• Triangle Aggregation

• Center of Gravity Estimation

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);

3

N

3

N

25

Perfect PIT

• 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

26

Departure test

Recognize directions of departure via neighbor exchange RSSI

• Problems with the assumption!!!

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

27

APIT approximation

Test only directions towards neighbors

Error in individual test exists, may be masked by APIT aggregation.

A

C

1

23

4

M

B

A

CB

A. Inside Case B. OutSide Case

1

23

4

M

28

APIT: Approximate PIT

• Distances unknown for most adjacent points

Use neighbor nodes only

• How to compare distances to beacons?

Stronger RSS

• Distributed algorithm:

Beacon nodes broadcast their location

Each node builds a table of beacons it receives and the corresponding RSS

Each node broadcasts this table once (1 hop only)

7mv1mv

7mv3mv

6mv2mv

SSn....SS1

Node M

3mv5623C

2mv3145B

1mv2020A

MySS(X,Y)

29

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

30

APIT Aggregation

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

31

Evaluation

• 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

32

Simulation 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

33

Error 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.

34

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 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

35

Error 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

36

Communication 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

37

Performance Summary

Centroid DV-Hop Amorphous APIT

Accuracy 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

Documents

Localization Murat Demirbas SUNY Buffalo. 2 Localization Localization of a node refers to the problem of identifying its spatial co-ordinates in some