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
Irregular range model (2/3)
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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
Irregular range model (3/3)
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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
Communication and sensing signal strength (2/4)
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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
Communication and sensing signal strength (3/4)
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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
Communication and sensing signal strength (4/4)
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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
Heterogeneous WSN deployment algorithm (2/9)
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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
Heterogeneous WSN deployment algorithm (3/9)
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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
Heterogeneous WSN deployment algorithm (4/9)
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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
Heterogeneous WSN deployment algorithm (5/9)
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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
Heterogeneous WSN deployment algorithm (6/9)
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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
Heterogeneous WSN deployment algorithm (7/9)
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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
Heterogeneous WSN deployment algorithm (8/9)
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Step 4: Scoring and deployment Deploy a new sensor node around the base
node
S1
S2
S0
Heterogeneous WSN deployment algorithm (9/9)
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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)
Polygon model (rotation steps = 4)
Polygon model (rotation steps = 8)
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 = 4)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
Results of Case 1 (2/3)
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Polygon model (rotation steps = 1)
Polygon model (rotation steps = 4)
Polygon model (rotation steps = 8)
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 = 4)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
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.
Results of Case 1 (3/3)
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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.
Disk modelPolygon model
(rotation steps = 1)Polygon model
(rotation steps = 4)Polygon model
(rotation steps = 8)Max. degree 6 ∞ 6 ∞ 6 ∞ 6 ∞
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.
Results of Case 2 (1/3)
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(rotation steps = 1)Polygon model
(rotation steps = 4)Polygon model
(rotation steps = 8)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
sensing coverage rate
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
deployed sensor nodes
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
Results of Case 2 (2/3)
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Polygon model (rotation steps = 1)
Polygon model (rotation steps = 4)
Polygon model (rotation steps = 8)
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
sensing coverage rate
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
deployed sensor nodes
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
Results of Case 2 (3/3)
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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
(rotation steps = 1)Polygon model
(rotation steps = 4)Polygon model
(rotation steps = 8)Max. degree 6 ∞ 6 ∞ 6 ∞ 6 ∞
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