Locating Sensors in the Wild: Pursuit of Ranging Quality

www.greenorbs.org

Locating Sensors in the Wild: Pursuit of Ranging Quality

Wei Xi, Yuan He, Yunhao Liu, Jizhong Zhao, Lufeng Mo, Zheng Yang, Jiliang Wang, Xiangyang Li

www.greenorbs.org 2

Outline• Motivation

• Observation on GreenOrbs

• Design of CDL

• Evaluation

• Ongoing work of GreenOrbs

www.greenorbs.org 3

GreenOrbs

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Existing approaches (1)• GPS

• Problems with tree covers

• Range-Based Approaches– TOA, TDOA, AOA

• Require extra hardware support• Expensive in manufactory cost and energy consumption

– RSSI-based• Based on the log-normal shadowing model• Inaccurate due to channel noise, interference, attenuation,

reflection, and environmental dynamics

00

( ) ( ) 10 logT

dRSSI d P PL d X

d

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Existing approaches (2)• Range-Free Approaches

– Rely on connectivity measurements – The accuracy is affected by node

density and network conditions

• RSD (SenSys’09)– Regulated signature distance

• SISR (MobiCom’09)– Merely differentiate good and bad links

DV-Hop

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• Design of CDL

• Evaluation


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Two-folded ranging quality

0 10 20 30 40 50 60 70 80 90-95

-90

-85

-80

-75

-70

-65

Distance Between Node Pairs (m)

RS

SI (

dB

m)

0 5 10 15 20-94

-92

-90

-88

-86

-84

-82

-80

-78

Time (hour)

RS

SI (

dB

m)

RSSI from B to ARSSI from A to B

0 5 10 15 200

50

100

Time (hour)

Hu

mid

ity (

%)

0 5 10 15 2020

40

Te

mp

era

ture

(。C

)

Temperature

Humidity

0 3 6 9 12 150

0.10.20.30.40.50.60.70.80.9

1

Mean ranging error (m)

CD

F (

×1

00

%)

1. Irregular 2. Dynamic3. Susceptible to the environment 4. Ubiquitous diverse

errors

10 20 30 40 50 60 70 80 90 1000

3

6

9

12

15

Node ID

Mea

n R

angi

ng e

rror

(m)

0 20 40 60 80 100-100

-95

-90

-85

-80

-75

-70

-65

Distance Between Node Pairs (m)

RS

SI (

dB

m)

Humidity:97%Humidity:43%Humidity:17%

Node location accuracy & range measurement accuracy

Fine-grained differentiation is necessary!

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• Design of CDL

• Evaluation


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1. Range-free localization: virtual-hop

2. Local filtration: two types of matching

3. Calibration: weighted robust estimation

Local Filtration

Neighborhood Hop-count Matching

Neighborhood Sequence Matching

Judgment

Virtual-hop Localization

Be a reference

Calibration

Do nothing

Good

Bad

Undertermined

Design of CDL

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DV-Hop

r

1

1

2

23

3

33

4

4

4

44

5

5

6

7

8

When non-uniform deployment is present, nodes with equal hop- counts often have different distances to the landmark(s).


1

1, ( )

, 01

, 01

jP

jk i ik jk i j

j

j

j

j j

jk

j

j

j j

VH VH LH v P whereP

NN

NLH

PN

P

Virtual-hop localization

For a node, its number of previous-hop or next-hop neighbors reflects the relative distance from the node to its parent node.

(1,7)

(2,1)

(2,2)

(4,0)

Landmark

va

vb

vp

vqRk


Virtual-hop vs. DV-hop

0 20 40 60 80 1000

5

10

15

20

25

30

Node ID

Loca

lizat

ion

Err

or (m

)

DV-hop

Virtual-hop

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Localization error (m)

CD

F (

×1

00

%)

DV-hopVirtual-hop

Compared with DV-hop, Virtual-hop reduces the localization errors by

10%~99%.


Local filtration (1)

0 20 40 60 80 1000

5

10

15

20

25

30

35

40

Node ID

Err

or

(m)

Virtual-hopIndiscriminate Calibration

Indiscriminate calibration probably reduces localization accuracy.


Local filtration (2)• Bad nodes exhibit more mismatches

• Neighborhood hop-count matching– Compare the real hop-distance with the one

calculated using estimated node coordinates

AD

C

B

EF

G

G’

D

C

B

EF

GA’

A

(a) A good node with one bad neighbor

(b) A bad node with six good neighbors


Local filtration (3)• Neighborhood sequence matching

AD

C

B

EF

G

G’

D

C

B

EF

GA’

A

NA B C D E F G

SA 6 5 1 2 4 3

SA’ 6 4 1 2 3 5

NA B C D E F G

SA 6 5 1 2 4 3

SA’ 4 2 1 3 6 5

Compare RSSI sequence with estimated distance sequence

i i iM CosDist 1 1 2 22 2 21 2

ni

a a a a a aCosDist

n

Matching degree


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

5

10

15

20

25

Matching degree τ

Est

imat

ed L

ocat

ion

Erro

r (m

)

0

0.5

1

i l

i l i u

i u

M

G M

M

According to the matching degree, we sort nodes into three classes Good Bad Undetermined

Local filtration (4)


• The basic objective function in LSE

• RQAC– Weight good nodes by good neighbors

– Differentiates links with different ranging qualities

* 2( , ), where ( , ) ( )ij ijj

f g i j g i j l d

1

1 , where ,

0 otherwise

jjk jk

j jk k jkk

l dG

2

2

( ) | |( , )

ln(| | 1) | |j ij ij ij ij

ij ij j ij ij

l d l dg i j

l d l d

Ranging-Quality Aware Calibration

1

j

jj

kk




• Design of CDL

• Evaluation



Evaluation• Setup

– Experiments• 100 GreenOrbs nodes (4 landmarks)

– Simulations• Randomly deploy 200~1000 nodes • A 500*500m2 square region • Transmission range: 30m


Comparison

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

X Axis (m)

Y A

xis

(m)

Real LocationLandmarkSISRCDL

Algorithm Error (m)

DV-HOP 8.7

MDS-MAP 5.9

SISR 5.6

CDL 2.9


CDF of localization errors

0 5 10 15 200

0.2

0.4

0.6

0.8

1

Localization error(m)Cum

ulat

ive

dis

trib

utio

n fu

nctio

n (×

100%

)

DV-hopMDS-MAP(C,R)SISRCDL


Efficiency of iteration

The number of good nodes quickly

increases as iterations go on.

1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

Iterations

Lo

caliz

atio

n E

rro

r (m

)

SISRCDL

1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

60

70

80

90

Iterations

Nu

mb

er

of N

od

es

Good

Bad

Undetermined

10 20 30 40 50 60 70 80 90 1001

2

3

4

5

6

7

8

9

10

Itera

tion

s

Node ID

Good Bad Undetermined


20 900

5

10

15

20

Humidity (%)

Lo

caliz

atio

n E

rro

r (m

)

DV-hopMDS-MAPSISRCDL

Humidity has a positive impact on the localization accuracy of all the four

approaches.

Impact of environmental factors


8 10 12 14 16 18 200

5

10

15

20

25

Node Density

Me

an

Lo

caliz

atio

n E

rro

r (m

)


4 6 8 10 12 14 160

10

20

30

40

50

Number of Landmarks

Me

an

Lo

caliz

atio

n E

rro

r (m

)


Increasing node density or landmarks yields better localization accuracy.

Impact of system parameters


Summary of CDL• GreenOrbs

– A most challenging scenario of WSN localization

• Our belief: ranging quality is two-folded– The location accuracy of the reference nodes– The accuracy of range measurements

• Combined and Differentiated Localization– VH localization addresses non-uniform deployment– Filtration picks good nodes with good location accuracy– RQAC emphasizes the contribution of the best range

measurements




• Design of CDL

• Evaluation



Ongoing work of GreenOrbs• New applications

– Carbon sink/emissions measurements– Forest fire risk prediction

• Research on WSN management


Thanks!Q & A

Locating Sensors in the Wild: Pursuit of Ranging Quality

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