HETEROGENEOUS WIRELESS SENSOR NETWORK DEPLOYMENT Yeh-Ching Chung Department of Computer Science...

HETEROGENEOUS WIRELESS SENSOR NETWORK

DEPLOYMENT

Yeh-Ching ChungDepartment of Computer

ScienceNational Tsing Hua University

Outline

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What is Wireless Sensor Network (WSN)? Heterogeneous WSN Irregular coverage model: polygon

model Irregular range model Heterogeneous WSN deployment

algorithm Experiments Conclusions

Wireless sensor network (1/2)

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A wireless network consists of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions [Wikipedia]. Resource-constrained sensor node:

Low-power microcontroller Constrained memory Low transmission bandwidth Limited power source (battery, solar panel)

Sensing AreaSensing Area

phenomenonphenomenon

SinksSinks

WSNWSN

Wireless sensor network (2/2)

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Enabling Technologies: Embedded system

Small form factor Wireless networking

WLAN, Bluetooth, ZigBee Sensing

Infrared, ultrasonic, temperature, acceleration, gas, …

MICAz by Crossbow

MICA2DOT by Crossbow

Applications of WSN

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Structural health monitoring Industrial equipment monitoring

Petroleum facility Semiconductor plant

Environmental monitoring Volcano monitoring Habitat monitoring

Others Military applications: target detection, classification, and

tracking Health applications: collect physiological data Air conditioner control in home/office buildings

Heterogeneous WSN

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WSN consists of sensor nodes with different characteristics: Coverage area

Different types of antennas and sensing devices result in various communication and sensing areas

Effective communication and sensing ranges Unavoidable variations for the same type of sensor nodes

Others Computing power: speed of microcontroller, size of

memory Energy consumption: battery powered, unlimited power

source

Deployment problems of heterogeneous WSN

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How to deploy a heterogeneous WSN to: Maintain network connectivity Get more sensing coverage rate

How to model the irregularity of sensor nodes? Different shapes of coverage areas Various effective communication and sensing

ranges

Maintain network connectivity

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Two-way communication

Heterogeneous WSN Homogeneous WSN

Disconnected

Get more sensing coverage rate

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Reduce the overlapping between sensor nodes

Lower sensing coverage rate Higher sensing coverage rate

Model the irregularity of sensor nodes (1/2)

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Disk model [Li et al. 2003]: The communication/sensing area of a sensor

node is represented by a circular area. Not practical to a realistic sensor node.

Helix antenna

Infrared sensor

Modeling?

communication area

sensing area

Model the irregularity of sensor nodes (2/2)

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Degree of irregularity (DOI) [He et al. 2005, Zhou et al. 2006]: Based on the disk model, denote the

irregularity of the radio propagation pattern: The maximum radio range variation per unit degree

changed from 0° to 360° The radius (effective communication/sensing

range) varies between pre-defined upper and lower bound.

Contributions

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Contributions of our work: Polygon model

Represent different shapes of communication and sensing areas of sensor nodes

Irregular range model Represent various effective communication and

sensing ranges for the same type of sensor nodes Heterogeneous WSN deployment algorithm

Topology control: maintain network connectivity Scoring process: improve sensing coverage gains

Polygon model (1/2)

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The polygon model is represented by a list of vertices: Modelpoly = {vexi = (rangei, i) | 1 i m, m ≥ 3}, where the

ith vertex of the polygon, vexi, is represented by the polar coordinate (rangei, i) rangei (radial coordinate): the default communication or sensing range

of a sensor node at i i (angular coordinate): the counterclockwise angle from 0°

An example: Modelpoly = {vex1, …, vex16} = {(range1,

1), …, (range16, 16)} = {(25, 0°), (20, 15°), (35, 30°), (50, 50°), (60, 70°), (65, 90°), (60, 110°), (50, 130°), (35, 150°), (20, 165°), (25, 180°), (15, 210°), (20, 230°), (10, 270°), (20, 310°), (15, 330°)}

0∘

15∘

30∘

50∘

70∘ 90∘

110∘

130∘

150∘

165∘

180∘

210∘ 230∘ 270∘ 310∘

330∘

vex1

vex16

Polygon model (2/2)

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Represent communication or sensing coverage area:

The sensing area of sensor node Sn :

Areapoly,S(Sn) =(Loc(Sn), {(RangeS(Sn)1, 1), …, (RangeS(Sn)m, m)}, Rot(Sn)) =((10, 20), {(25.3, 0°), …, (14.9, 330°)}, 30°)

Where RangeS(Sn)i is the effective sensing range of Sn at i

Sn 0°

Rot (Sn) = 30°

vex1

Loc(Sn) = (10,20)

Irregular range model (1/3)

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The effective communication or sensing range of a sensor node Sn at i is defined as: Range(Sn)i = rangei + Rand(DOI), –3×DOI ≤

Rand(DOI) ≤ 3×DOI rangei: the default communication/sensing range at i

DOI : the degree of irregularity of sensor node Sn

Rand(DOI): the normal distribution with the standard derivation DOI


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Why 3×DOI ? The “68-95-99.7 rule” in normal distribution:

99.7% of the effective communication/sensing ranges are within three standard derivations (3*DOI) away from the mean value (the default communication/sensing range)

Normal (Gaussian) Distribution

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

18 21 24 27 30 33 36 39 42

Range (S n )

Pro

babi

lity

dens

ity

range(S n ) (Mean) = 30DOI (Standard deviation) = 3.0

The value of Range(Sn) is between 21 and 39


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The value of DOI determines the irregularity of coverage areas of sensor nodes.

(a) DOI = 0 (b) DOI = 1 (c) DOI = 2 (d) DOI = 3

Sensor node connection

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The connection degree of a sensor node Sn, Deg(Sn): The number of two-way communication links to

Sn

The maximum connection degree, Degmax(Sn): The maximum number of sensor nodes that can be

connected to Sn

Communication and sensing signal strength (1/4)

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The degree of the received communication or sensing signal at a point from a sensor node.

Used by the proposed heterogeneous WSN deployment algorithm: Topology control Coverage rate calculation


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Based on the Friis transmission formula [Friis 1946]: Powerr / Powert = Arear Areat / d2 λ2

Powert: the power fed into the transmitting antenna

Powerr: the power available at the receiving antenna

Arear (or Areat): the effective area of the receiving (or transmitting) antenna

d: the distance between two antennas λ: the wavelength

Assume that Powert, Arear, Areat, and λ are constants, Powerr 1/d2


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The communication and sensing signal strength of Sn at a point Pi are defined as:

RangeC(Sn, Pi) and RangeS(Sn, Pi): the effective communication and sensing range of Sn at Pi

d(Sn, Pi): the Euclidean distance between Sn and Pi

)()( if 0

)()( if ))()(()(

2

inCin

inCinininCin

P,SRangeP,Sd,

P,SRangeP,Sd,P,Sd/P,SRangeP,SCSS

)()( if 0

)()( if ))()(()(

2

inSin

inSinininSin

P,SRangeP,Sd,

P,SRangeP,Sd,P,Sd/P,SRangeP,SSSS


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The relationship between SSS(Sn, Pi) and d(Sn, Pi): If d(Sn, Pi) ≤ RangeS(Sn, Pi), it indicates that Pi is

covered by the sensing area of Sn

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34Distance d (S n, P i)

Sen

sing

sig

nal s

tren

gth

Sen

(Sn,P

i)

Covered Uncovered

RangeS(Sn, Pi) = 20

Calculate the effective communication/sensing range (1/2)

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Calculate the effective sensing range of Sn at Pi, RangeS(Sn, Pi): Qi is the intersection point of ray

SnPi and line segment vexavexb

Note:

1. vexa = (RangeS(Sn)a, a+Rot(Sn)) and vexb = (RangeS(Sn)b, b+Rot(Sn))

2. The area of ∆vexaSnvexb is the sum of the area of ∆vexaSnQi and ∆QiSnvexb

0° Sn

Rot(Sn)

(Sn, Pi)

Pi

Qi

vexb

vexa

),(),( ininS QSdPSRange

)))((-),((sin)(-)))((-),((sin)(

)-(sin))()((

nbinbnSnainanS

abbnSanS

SRotPSSRangeSRotPSSRange

SRangeSRange

Calculate the effective communication/sensing range (2/2)

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An example: Given vex4 = (50, 80°), vex5 = (60, 100°), and (S1,

P1) = 90°

Since 80° < (S1, P1) < 100°, RangeS(S1, P1) = d(S1, P2) = (50∙60)∙sin(100°–80°) / [50∙sin(90°–80°) – 60∙sin(90°–100°)] ≈ 53.7 units

S1

P1

P2

vex4 = (50, 80°)

vex5 = (60, 100°)

0°

Rot (S1) = 30°

θ (S1, P1) = 90°

vex1

Calculate the sensing coverage rate

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The sensing coverage rate of the deployment area:

)(

)()(

deploygrid

deploycovdeployrate AreaN

AreaNAreaCov

Ngrid(Areadeploy): the number of grid points within the deployment area

Ncov(Areadeploy): the number of the grid point Pi within the deployment area with SSS(Pi) ≥ 1

deployment area

Heterogeneous WSN deployment algorithm (1/9)

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Preliminaries The sink node contains 1 communication

device (without sensing device) Each sensor node contains 1 communication

and 1 sensing devices The same type of communication/sensing

devices may have different communication/sensing ranges based on the value of degree of irregularity


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Given A deployment area with obstacles Multiple types of sensor nodes

Objectives A communication-connected WSN Higher sensing coverage rate with less sensor

nodes


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Step 1: Initialization Deployment area Deployable sensor nodes

An initialized deployment area:

1 sink node (S0)

1 pre-deployed sensor node (S1)

2 obstacles with different shapes

S0S1


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Step 2: Base node selection Select from the deployed sensor nodes Calculate deployment quota

Starting from the sink nodeTraverse along the communication links

Given Deg(S0) = 1, Degmax(S0) = 2

Deployment quota at S0 =

Degmax(S0) – Deg(S0) = 1

(Can deploy 1 more sensor node around S0)

S0S1


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Step 3: Candidate positions generation Generate candidate positions around the base node Based on the topology control mechanism

S0S1

P0

P1 P2

Given Max(CSS) = 4 and Max(SSS) = 2

P0 is abandoned: CSS(S0, P0) = 4.5 > Max(CSS)

P1 is abandoned:SSS(S1, P1) = 2.5 > Max(SSS)

For P2, CSS(S0, P2) = 1.5, SSS(S1, P2) = 0

P2 is selected as the candidate position around S0


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Step 4: Scoring and deployment Score(Sn, Pi): the increased sensing coverage if a

deployable sensor node Sn deployed at candidate position Pi

Given a deployable sensor node S2

Put a square area of grid points centered at P2, the length of edge = 2*Max(sensing range of S2)

Nbefore(Areasq(S2, P2)) = 250 (The number of grid points Gi with SSS(Gi) ≥ 1)

S0S1

P2


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Step 4: Scoring and deployment Select a candidate position & deployable sensor

node with the highest score

Rotate 0°Nafter(Areasq(S2, P2)) = 600 (points)Score(S2, P2) = 600 - 250 = 350

S0S1

P2

S1

P2

S0

S2 S2

Rotate 200°Nafter(Areasq(S2, P2)) = 950 (points)Score(S2, P2) = 950 - 250 = 700


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Step 4: Scoring and deployment Deploy a new sensor node around the base

node

S1

S2

S0


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Steps Initialization Base node selection Candidate positions generation Scoring and deployment

Stop deployment The limit of deployable sensor nodes is reached No more sensor nodes can be deployed

Stop conditions are not reached

Experiments

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(0, 0)

Sink node: S0(150, 150)

(500, 500)

Deploy different types of sensor nodes to an area with 9 obstacles

Four types of sensor nodes used for deploymentType 1: loop antenna + infrared sensorType 2: loop antenna + ultrasonic sensorType 3: chip antenna + infrared sensorType 4: chip antenna + ultrasonic sensor

(a) loop antenna (b) chip antenna (c) infrared sensor (d) ultrasonic sensor

Representation of coverage areas

Coverage area

Disk model Polygon model

loop antenna radius = 50.8 16 vertices: {(50.8, 9°), (50.8, 33.7°), (50.8, 56.3°), (50.8, 82°),(50.8, 98°), (50.8, 123.7°), (50.8, 146.3°), (50.8, 171°),(50.8, 189°), (50.8, 213.7°), (50.8, 236.3°), (50.8, 262°),(50.8, 278°), (50.8, 303.7°), (50.8, 326.3°), (50.8, 351°)}

chip antenna radius = 50.8 26 vertices: {(50.8, 6.8°), (50.2, 21.4°), (47.6, 38°), (43.7, 54.5°), (38, 68.2°), (28.7, 81.9°), (10.5, 90°), (28.7, 98.1°), (38, 111.8°), (43.7, 125.5°), (47.6, 142°) (50.2, 158.6°), (50.8, 173.2°), (50.8, 186.8°), (50.2, 201.4°), (47.6, 218°), (43.7, 234.5°), (38, 248.2°), (28.7, 261.9°), (10.5, 270°), (28.7, 278.1°), (38, 291.8°), (43.7, 305.5°), (47.6, 322°), (50.2, 338.6°),(50.8, 353.2°)}

infrared sensor radius = 47.7 3 vertices: {(47.7, 19.6°), (0, 180°), (47.7, 340.4°)}

ultrasonic sensor

radius = 40.1 11 vertices: {(40.1, 4.3°), (35.9, 17.9°), (31, 24.9°), (21.8, 35.8°), (13, 48°), (0, 180°), (13, 312°), (21.8, 324.2°}, (31, 335.1°) , (35.9, 342.1°) , (40.1, 355.7°)}

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Test sets of experiments Case 1: single type of deployable sensor

nodes Includes test sets 1 to 4 with single type of

sensor nodes

Case 2: multiple types of deployable sensor nodes Includes test sets 5 to 9 with two or four

types of sensor nodes

Set Sensor nodes

1 Type 1

2 Type 2

3 Type 3

4 Type 4

5 Type 1 + Type 2

6 Type 3 + Type 4

7 Type 1 + Type 3

8 Type 2 + Type 4

9Type 1 + Type 2 + Type 3 + Type

4

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Deployment parameters for each case:

1.Deployable sensor nodes: 600 for each type2.DOI: 0, 23.The maximum connection degree: 6, ∞4.The rotation steps of coverage areas: 1, 4, 8

Case 1

Case 2

Experiment analysis

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After deployment, we compare the accuracy of the proposed polygon model with the disk model: The sensing coverage rate The number of deployed sensor nodes The network connectivity

The network connectivity The number of isolated networks

An isolate network is a network in which sensor nodes of the isolated network cannot communicate with the sink node

Results of Case 1 (1/3)

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Polygon model (rotation steps = 1)



Max. degree 6 ∞ 6 ∞ 6 ∞ 6 ∞Set DOI = 01 0.3827 0.3878 0.7630 0.8245 0.8323 0.8914 0.7952 0.90172 0.5480 0.5505 0.8090 0.8936 0.8104 0.9166 0.7638 0.91903 0.3827 0.3878 0.8267 0.9114 0.9391 0.9686 0.9515 0.97094 0.5480 0.5505 0.8778 0.9289 0.9547 0.9816 0.9716 0.9863

Set DOI = 21 0.3540 0.3814 0.7628 0.8334 0.8855 0.9086 0.8209 0.91002 0.4793 0.4962 0.8300 0.8988 0.8982 0.9295 0.8900 0.94043 0.3585 0.4064 0.7907 0.9081 0.9624 0.9717 0.9706 0.97924 0.4943 0.4994 0.7708 0.9381 0.8954 0.9876 0.9806 0.9917

sensing coverage rate

Disk modelPolygon model

(rotation steps = 1)Polygon model


(rotation steps = 8)Max. degree 6 ∞ 6 ∞ 6 ∞ 6 ∞

Set DOI = 01 116 115 276 319 279 331 261 3382 151 153 273 319 265 331 238 3313 116 115 378 591 418 544 434 5254 151 153 386 571 424 549 444 536

Set DOI = 21 94 105 276 330 300 335 269 3332 127 132 277 321 293 336 285 3373 98 112 350 550 439 520 437 5214 133 135 334 571 384 545 456 533

deployed sensor nodes

The polygon model can deploy more sensor nodes and produces higher sensing coverage rate than the disk model for all test sets


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Max. degree 6 ∞ 6 ∞ 6 ∞ 6 ∞Set DOI = 01 0.3827 0.3878 0.7630 0.8245 0.8323 0.8914 0.7952 0.90172 0.5480 0.5505 0.8090 0.8936 0.8104 0.9166 0.7638 0.91903 0.3827 0.3878 0.8267 0.9114 0.9391 0.9686 0.9515 0.97094 0.5480 0.5505 0.8778 0.9289 0.9547 0.9816 0.9716 0.9863

Set DOI = 21 0.3540 0.3814 0.7628 0.8334 0.8855 0.9086 0.8209 0.91002 0.4793 0.4962 0.8300 0.8988 0.8982 0.9295 0.8900 0.94043 0.3585 0.4064 0.7907 0.9081 0.9624 0.9717 0.9706 0.97924 0.4943 0.4994 0.7708 0.9381 0.8954 0.9876 0.9806 0.9917






Set DOI = 01 116 115 276 319 279 331 261 3382 151 153 273 319 265 331 238 3313 116 115 378 591 418 544 434 5254 151 153 386 571 424 549 444 536

Set DOI = 21 94 105 276 330 300 335 269 3332 127 132 277 321 293 336 285 3373 98 112 350 550 439 520 437 5214 133 135 334 571 384 545 456 533


Type 1 and Type 3 sensor nodes are identical under the disk model:

The communication areas of the loop antenna and chip antenna are the same under the disk model.


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The WSN constructed under the polygon model are connected (no isolate networks) for all test sets.

The value of DOI affects the network connectivity under the disk model.





Set DOI = 01 0 0 0 0 0 0 0 02 0 0 0 0 0 0 0 0 3 10 11 0 0 0 0 0 0 4 2 3 0 0 0 0 0 0

Set DOI = 21 8 4 0 0 0 0 0 02 0 0 0 0 0 0 0 0 3 30 25 0 0 0 0 0 0 4 12 9 0 0 0 0 0 0

number of isolated networks

IncreasedType 3 and Type 4 sensor nodes use

Chip antenna

The maximum connection degree

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The restriction of the maximum connection degree may block the deployment of new sensor nodes.

The 25th deployed sensor node (S25) has 7 neighbors (S0, S2, S6, S7, S9, S13, and S35)

Since the maximum connection degree = 6, it is not possible to deploy new sensor nodes around S25.

As a result, no sensor nodes can be deployed into the empty area.


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Set DOI = 05 0.3827 0.3878 0.7988 0.8815 0.8114 0.9138 0.8899 0.91756 0.3827 0.3878 0.8530 0.9294 0.9667 0.9749 0.9831 0.98507 0.3827 0.3878 0.7630 0.8245 0.8323 0.8914 0.7952 0.90288 0.5480 0.5505 0.8090 0.8936 0.8104 0.9166 0.7638 0.91909 0.3827 0.3878 0.7988 0.8815 0.8114 0.9138 0.8899 0.9223

Set DOI = 25 0.3540 0.3814 0.8510 0.8936 0.9045 0.9274 0.9009 0.93416 0.3585 0.4064 0.8403 0.9364 0.9183 0.9875 0.9761 0.99047 0.3720 0.3796 0.7661 0.8493 0.8611 0.9543 0.9316 0.95828 0.4942 0.4818 0.8920 0.9046 0.9319 0.9646 0.9493 0.97009 0.3720 0.3796 0.8653 0.9168 0.9393 0.9635 0.8805 0.9707


Set DOI = 05 116 115 269 314 265 337 287 3286 116 115 360 567 432 515 464 5367 116 115 276 319 279 331 261 3388 151 153 273 319 265 331 238 3319 116 115 269 314 265 337 287 341

Set DOI = 25 94 105 274 323 291 336 288 3356 98 112 369 547 407 522 445 5317 103 104 302 365 308 397 345 3968 128 130 340 381 345 402 358 4129 103 104 321 394 342 402 309 403


Similar to Case 1, the polygon model can deploy more sensor nodes and produces higher sensing coverage rate than the disk model for all test sets


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Max. degree 6 ∞ 6 ∞ 6 ∞ 6 ∞Set DOI = 05 0.3827 0.3878 0.7988 0.8815 0.8114 0.9138 0.8899 0.91756 0.3827 0.3878 0.8530 0.9294 0.9667 0.9749 0.9831 0.98507 0.3827 0.3878 0.7630 0.8245 0.8323 0.8914 0.7952 0.90288 0.5480 0.5505 0.8090 0.8936 0.8104 0.9166 0.7638 0.91909 0.3827 0.3878 0.7988 0.8815 0.8114 0.9138 0.8899 0.9223

Set DOI = 25 0.3540 0.3814 0.8510 0.8936 0.9045 0.9274 0.9009 0.93416 0.3585 0.4064 0.8403 0.9364 0.9183 0.9875 0.9761 0.99047 0.3720 0.3796 0.7661 0.8493 0.8611 0.9543 0.9316 0.95828 0.4942 0.4818 0.8920 0.9046 0.9319 0.9646 0.9493 0.97009 0.3720 0.3796 0.8653 0.9168 0.9393 0.9635 0.8805 0.9707


Set DOI = 05 116 115 269 314 265 337 287 3286 116 115 360 567 432 515 464 5367 116 115 276 319 279 331 261 3388 151 153 273 319 265 331 238 3319 116 115 269 314 265 337 287 341

Set DOI = 25 94 105 274 323 291 336 288 3356 98 112 369 547 407 522 445 5317 103 104 302 365 308 397 345 3968 128 130 340 381 345 402 358 4129 103 104 321 394 342 402 309 403


Only one type of sensor nodes are deployed in these test sets:

Set 5: Type 1Set 6: Type 3 (same as Type 1 in Disk model)Set 7: Type 1Set 9: Type 1


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The polygon model is more accurate than the disk model while multiple types of sensor nodes are deployed.

The number of isolated networks under the disk model is increased while the value of DOI is changed from 0 to 2.

number of isolated networks Disk modelPolygon model




Set DOI = 05 0 0 0 0 0 0 0 06 10 11 0 0 0 0 0 07 0 0 0 0 0 0 0 08 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0

Set DOI = 25 8 4 0 0 0 0 0 06 30 25 0 0 0 0 0 07 23 16 0 0 0 0 0 08 6 7 0 0 0 0 0 09 23 16 0 0 0 0 0 0

Set 6 consists of Type 3 and Type 4 sensor nodes that use Chip

antenna

Conclusions

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The proposed irregular coverage model - polygon model, can represent different shapes of communication and sensing areas of sensor nodes.

The four-step heterogeneous WSN deployment algorithm can maintain the network connectivity and improve the sensing coverage gains. Topology control mechanism and scoring process

According to the simulation results, the proposed polygon model is more accurate than the disk model. Communication-connected WSN Higher sensing coverage rate

HETEROGENEOUS WIRELESS SENSOR NETWORK DEPLOYMENT Yeh-Ching Chung Department of Computer Science...

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