International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume - 2 | Issue – 4
Review Paper on Wir
Wireless
eless Sensor Networks (WSN) for
Fuzzy Logy Algorithms
Ashish Verma1, Mukesh Patidar2
1
M. Tech (Student), 2Professor
Department of Electronics and Communication Engineering
Engineering, Lakshmii Narain College of Technology,
Indore, Madhya Pradesh, India
ABSTRACT
Wireless Sensor Networks is a group of spatially
dispersed dedicated sensors to monitor and record an
environment’s physical conditions and to organize
collected data at a central location (Xiao 2004).
Wireless Sensor networks (WSNs) have become one
of the most interesting areas of research in the past
few
ew years. A WSN is composed of a number of
wireless sensor nodes which form a sensor field and a
sink. These large numbers of nodes, having the
abilities to sense their surroundings, perform limited
computation and communicate wirelessly form the
WSNs. It is enjoy great benefits due to their very low
cost, small scale, smart sensor nodes etc.
Applications of wireless sensor networks such as:
span environmental, animal monitoring, industrial
monitoring, agricultural monitoring, automation,
health monitoring and many other areas. In this paper
review on WSN, literature and analysis of fuzzy loge
algorithm for consider different numbers of Node and
cluster like 100, 200 and 300.
Keywords: WSN, Cluster, Fuzzy logy, Sensor
I.
INTRODUCTION
Wireless sensor network (WSN) is an ad
ad-hoc network
technology comprising even thousands of autonomic
and self-organizing
organizing nodes that combine environmental
sensing, data processing, and wireless networking.
The applications for sensor networks range from
homee and industrial environments to military uses.
Unlike the traditional computer networks, a WSN is
application-oriented
oriented and deployed for a specific task.
WSNs are data centric, which means that messages
are not send to individual nodes but to geographical
locations or regions based on the data content. A
WSN node is typically battery powered and
characterized by extremely small size and low cost.
Fig. 1: Cluster based WSN
It is wireless network sometimes more specifically
referred as Wireless Sensor and Actuator Networks as
described in a Wireless sensor networks (WSNs)
enable new applications and require non-conventional
non
paradigms for protocol design due to several
constraints. Wireless sensor network are using Global
Positioning System (GPS) and local positioning
algorithms can be used to positioning information and
obtain location. The requirement for low device
complexity together with low energy consumption, a
proper balance between communication and
signal/data processing capabilities must be found.
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
several sensor network applications such as inter
cluster communication, node localization and so on.
Clustering algorithms have extensive applications in
the precedent years and common clustering
algorithms have been proposed for energy
consumption in recent years in all of these algorithms,
and nodes are structured as clusters, superior energy
nodes are called as Cluster Head (CH) and other
nodes are called as normal sensor nodes.
Fig. 2: WSN Technology
In recent years an efficient design of a Wireless
Sensor Network has become a leading area of
research. A Sensor is a device that responds and
detects some type of input from both the physical or
environmental conditions, such as pressure, heat,
light, etc. The output of the sensor is generally an
electrical signal that is transmitted to a controller for
further processing.
II. Types of WSNs (Wireless Sensor Networks)
Depending on the environment, the types of
networks are decided so that those can be deployed
underwater, underground, on land, and so on.
Different types of WSNs include:
1. Terrestrial WSNs
2. Underground WSNs
3. Underwater WSNs
4. Multimedia WSNs
5. Mobile WSNs
II. ENERGY CONSUMPTION
Wireless Sensor Network (WSN) plays an extremely
significant role in usual lives. Wireless Networks in
provisions of constraints of their resources. The
energy consumption is the principal concern in
Wireless Sensor Network (WSN). Therefore, a
numerous researchers focused on energy efficient
algorithms in WSNs for extending the life time of
sensors. These differ depending on the deployment of
node, the network design, the characteristics of the
cluster head nodes and the network operation. Energy
is proficient of save by grouping nodes as clusters.
A. Cluster Head
Clustering is used in order to advance the scalability
of network performance. Clustering is useful in
III. LITERATURE REVIEW
1. D. Naga Ravikiran and C. G. Dethe (2018)
“Improvements in Routing Algorithms to Enhance
Lifetime of Wireless Sensor Networks”
International Journal of Computer Networks &
Communications (IJCNC) Vol.10 (2) 2018. In this
paper improvements in various parameters are
compared for three different routing algorithms.
First, it is started with Low Energy Adaptive
Cluster Hierarchy (LEACH)which is a famed
clustering mechanism that elects a CH based on
the probability model. Then, work describes a
Fuzzy logic system initiated CH selection
algorithm for LEACH.
2. Vineet Mishra et al. (2015). “Performance
Analysis Of Cluster Formation In Wireless Sensor
Networks”, Global Journal of Advance
Engineering Technologies and Sciences, 2(11).
The energy consumption is the principal concern
in WSN. It system is built on an IEEE 802.15.4
wireless mesh network. Wireless Sensor
Networking is a network of wireless sensor nodes
deployed in an area. The wireless sensor network
consists of the sensor nodes. The idea of
development of wireless sensor networks was
initially motivated by military applications. A
WSN provides a reliable, low maintenance, low
power method for making measurements in
applications where cabled sensors are impractical
or otherwise undesirable. A wireless sensor
network (WSN) is a wireless network using
sensors to cooperatively monitor physical or
environmental conditions such as humidity,
pressure, temperature, sound, vibration.
3. T. Arivanantham et al. (2014). “Clustering
Techniques to Analyze Communication Overhead
in Wireless Sensor Network”, International
Journal of Computational Engineering Research,
4(5). The major problem with wireless sensor
network is their limited source of energy, the
courage constraint and high traffic load. In this
paper we introduce various clustering techniques
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
which are to be used to reduce communication
overhead and increase network’s lifetime. In the
present work, the comparative evaluation of
communication overhead for the wireless sensor
network based on clustering technique is carried
out. As a result of these experiments, we evaluated
the communication overhead in WSN using Kmeans clustering algorithm and Fuzzy clustering
algorithm.
4. Labisha R.V et al. (2014). “Energy Efficient
Clustering Algorithms in Wireless Sensor
Networks-An Analytical View” International
journal 9(3). Wireless sensor network refers to a
group of spatially distributed and dedicated
sensors for monitoring and recording the physical
conditions of environment like temperature,
humidity, sound, pollution levels, and wind speed
with direction and pressure. Sensors are self
powered nodes which also possess limited
processing
capabilities
and
the
nodes
communicate wirelessly through a gateway. The
capability
of
sensing,
processing
and
communication found in sensor networks lead to a
vast number of applications of wireless sensor
networks in areas such as environmental
monitoring, warfare, education, agriculture to
name a few. In the present work, the comparative
evaluation of communication overhead for the
wireless sensor network based off on clustering
technique is carried out.
5. Amrinder Kaur et al. (2013). “Simulation of Low
Energy Adaptive Clustering Hierarchy Protocol
for Wireless Sensor Network” International
Journal of Scientific Engineering, 3(7). Recent
advances in wireless sensor networks have settled
into an important part of one’s everyday life and
gained attention from both the research
community and actual users. In wireless sensor
networks energy alertness is an essential
consideration & it explored to many new
protocols specifically designed for sensor
networks. This paper presents the overview of
wireless sensor networks and their routing
challenges and design issues that is faced in
designing a protocol for sensor networks.
6. Anshul Shrotriya et al. (2013). “Energy Efficient
Modeling of Wireless Sensor Networks Based on
Different Modulation Schemes Using QualNet”
International Journal of Scientific Engineering and
Technology, 1(3). I have study in the paper that it
account for the analysis of energy consumption in
existing energy models of Wireless Sensor
Network (WSN) based upon different modulation.
There are two main energy models preferred in
Wireless Sensor Network, MOTES and MICAZ.
So here, we have investigated the more energy
efficient energy model under different modulation
schemes. So I can simply find the better energy
model as well as the better modulation scheme to
efficiently utilize the energy in WSN. It has been
found that, based upon the modulation schemes;
mica z performs better than mica mote energy
model.
7. Shiv Prasad Kori et al. (2013). “Performance
Comparison in Terms of Communication
Overhead for Wireless Sensor Network Based on
Clustering Technique” International Journal of
Electronics Communication and Computer
Engineering, 4(3). The present work, the
comparative evaluation of communication
overhead for the wireless sensor network based of
on clustering technique is carried out. It has been
be observed that overhead in cluster based
protocol is not much dependent upon update time.
Simulation a result indicates that cluster based
protocol has low communication overheads
compared with the BBM based protocol when
sink mobility is high. In these applications a large
number of sensors are expected, requiring careful
architecture and management of the network.
Grouping nodes into clusters has been the most
popular approach for support scalability in WSNs.
Significant attention has been paid to clustering
strategies and algorithms yielding a large number
of publications.
IV. METHODOLOGY
The System has been implemented in the MATLAB.
The wireless sensor network is design with following
specification in table 1. The method of design
simulation has been given below:
Table 1: Specification Fuzzy Algorithms
S. No.
Specification
Value
1
NO. of Node and 200, 300 and
cluster
400
2
Length of network 1×1m
area
3
Maximum range
500 m
4
5
6
7
Noise power in dBm
Transmitted power
Operating frequency
Technology
50 dbm
1 Mw
2.4 GHZ
Fuzzy
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
A. Fuzzy C-Means Clustering
This algorithm works by assigning membership to
each data point corresponding to each cluster center
on the basis of distance between the cluster center and
the data point. For more the data is near to the cluster
center more is its membership towards the particular
cluster center. In clearly, summation of membership
of each data point should be equal to one.
1000
900
88
60
91 25
64
93
31
70
4
16
800
700
2
600
68
3
400
1
8
54
74
71
95
76
47 77
23
18
92
13
200
62
100
80
11
78
87 58
36
73
49
90
38
37
40
66
86
75
26
79
97
41
98
55
24
15
5
100
10
52
33
19
14
53
200
300
69
83
4627
400
500
600
85
8434
51
21
67
45
89
29
63
0
81
43
61 99
94 3042
57
65
17
12 82
300
100
35
50
7
39
22
59 72
9
48
28
500
0
32
446
56
20
700
800
900
96
1000
Fig: 3: Performance of Fuzzy clustering for 100
Nodes
1000
130
169
129 195
143
96
93
37
100 197 51 75
77
38
12
163
147
110
900
192 186
132
43
187
74
200
157
85
112126
114
173
800
73
80
182
165 166 76
156
155
111 125
11514
138
159
42
97 177
150 170
181
36 136
17
4189 24
47
104
174
700
9164
55
22
145
31 844
92
52
193106
40
600
58
144
33
107
46 81
196
168
108
71 83
60
146
34
191
7
148
178 188
53
134
4
500
48
167
162
183
105 82
1711813
6
95
194
109
11
78
87
180
29
198
23
20137
400 153
120
160
50
161
39
79
68
94
27
189
45
3
185 59
300
19
172
152 88
69
184
15116
54
158 131
151
61
135
133
66
10
200
142139
98
56
21
49
199
5
32
117 128
30118
123103
2
90
102
72
122
140
84
6263
164
57
127
179
86
175 99
16
100
35
154101 141
1
176
26
70 121 28
25
65
190 149 67
9
124
119
113
0
0
100
200
300
400
500
600
700
800
900 1000
Fig: 4: Performance of Fuzzy clustering for 200
Nodes
1000
30
19595 128
69
263
29 261
165 43
237
299
145 97242
120 235
180
11963110266 219 182223
116
103
233
207
47283
255
262 169 254 3210 192
159
130
900
92
244
122
161
86
101 206
87
194
250
178
231
104 17611173
57 197
12
280
14 100
26 102
249
136
800
173
8265
66
162
216
56 55
227
77
205
1115
8 121
38
131164
221 59239
17125
258
81
70270
247
259 74 50
36
4
251
700
218 260
134
45
300
291 177
228
297
285
225
264
213
93 124
222
208
236
181
600
6
44
7
253
267
96
68
109
139
243 275
46
132
232 163 294
91
202
168
48
108
295
78
198
88
241 276
34
277
500 196
67
62
252
190
105
298
5
71 83
147
144
185
238
9 230
94
106
107
80
226
279
200 215203
129
98
245
53
175
214
400
286 183
37149
172 20
273
246
113
60
186
158
265
210
61
118 140 292
117 248
151
89
187 170 85
300
127
142
16
72
268 52
229
155
84
272256
112220
58
135193
51
123
31
240
166
217
35
33
293
200 209
42 99
167
18
28
39
189
284
17
23
157
274
3 2 133
11522
148
199137 138 40
282
281
174
271
114 152 184 288
64
204
143
269
79
7527
100 153
290
141
278
154
541
296
7641
212
191
21
125
257
224
211 234
49
289
90
201
150
156
24
160
188
287
126
146
19
13
179
0
0
100 200 300 400 500 600 700 800 900 1000
Fig: 5: Performance of Fuzzy clustering for 300
Nodes
VI. CONCLUSION
Wireless network is considered as most common
service used in industrial and commercial application
due to its technological enhancement in the process,
interaction and utilisation of low power embedded
computational devices. Wireless sensor networks
(WSNs) utilize fast, cheap, and effective applications
to imitate the human intelligence capability of sensing
on a wider distributed scale The energy consumption
reduction has been taken as a objective in this project.
The clustering based network architecture has been
proposed in this project to reduce the communication
overhead which in turn reduces the energy
consumption. The various clustering techniques are
available for locating the access point for WSN.
Following path is generated for consider number of
nodes 100, 200 and 300 respectively using Matlab toll
R2013a.
Path= 6, 57, 18, 8 (Fig. 3)
Path= 6, 18, 27, 8 (Fig. 4)
Path= 6, 295, 175, 8 (Fig. 5)
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International Journal of Computer Networks &
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