WSNs is a group of inexpensive and autonomous sensor nodes interconnected and work together to monitor various environmental conditions such as humidity, temperature, pressure, and other climate changes. Wireless sensor nodes are randomly installed and communicate themselves through the wireless communication medium. In this paper we compared the simulation results of the K-mean techniques for different numbers of nodes like 50, 100 and 150. Subham Bhawsar | Aastha Hajari "Performance Analysis of K-mean Clustering Map for Different Nodes" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18859.pdf

1. International Journal of Trend in
International Open Access Journal
ISSN No: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
Performance Analysis of K
Subham Bhawsar, Aastha Hajari
Embedded System and VLSI Design
Shiv Kumar Singh Institute of Technology
ABSTRACT
WSNs is a group of inexpensive and autonomous
sensor nodes interconnected and work together to
monitor various environmental conditions such as
humidity, temperature, pressure, and other climate
changes. Wireless sensor nodes are randomly installed
and communicate themselves through the wireless
communication medium. In this paper we compared
the simulation results of the K-mean techniques for
different numbers of nodes like 50, 100 and 150.
Keyword: K-mean, Nodes, Clusters.
I. INTRODUCTION
The Wireless sensor networks (WSN) are highly
distributed networks of small, lightweight nodes and
deployed in large numbers to monitor the
environment parameters or system by the
measurement of physical parameters such as
temperature, pressure, or relative humidity [2]. Each
node of the network consists of three subsystems: the
sensor subsystem which senses the environment, the
processing subsystem which performs local
computation on the sensed data, and the
communication subsystem which is responsible for
message exchange with neighbour sensor nodes.
While individual sensors have limited sensing region,
processing power, and energy, networking a large
numbers of sensors gives rise to robust, reliable, and
accurate sensor network covering a wider region [2]
Wireless Sensor Networks (WSNs) typically consist
of a large number of low-cost, low
multifunctional wireless sensor nodes. Nodes are
equipped with sensing, communication and
computation capabilities, where they communicate via
a wireless medium and work collaboratively to
accomplish a common task.
International Journal of Trend in Scientific Research and Development (IJTSRD)
International Open Access Journal | www.ijtsrd.com
ISSN No: 2456 - 6470 | Volume - 2 | Issue – 6 | Sep
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
Performance Analysis of K-mean Clustering Map for Different Nodes
Subham Bhawsar, Aastha Hajari
edded System and VLSI Design, Department of Electronics and Communication Engineering
Shiv Kumar Singh Institute of Technology & Science (SKSITS), Indore, Madhya Pradesh
WSNs is a group of inexpensive and autonomous
sensor nodes interconnected and work together to
monitor various environmental conditions such as
pressure, and other climate
changes. Wireless sensor nodes are randomly installed
and communicate themselves through the wireless
communication medium. In this paper we compared
mean techniques for
s like 50, 100 and 150.
The Wireless sensor networks (WSN) are highly
distributed networks of small, lightweight nodes and
deployed in large numbers to monitor the
environment parameters or system by the
measurement of physical parameters such as
e humidity [2]. Each
node of the network consists of three subsystems: the
sensor subsystem which senses the environment, the
processing subsystem which performs local
computation on the sensed data, and the
communication subsystem which is responsible for
message exchange with neighbour sensor nodes.
While individual sensors have limited sensing region,
processing power, and energy, networking a large
numbers of sensors gives rise to robust, reliable, and
accurate sensor network covering a wider region [2].
Wireless Sensor Networks (WSNs) typically consist
cost, low-power and
multifunctional wireless sensor nodes. Nodes are
equipped with sensing, communication and
computation capabilities, where they communicate via
m and work collaboratively to
To get more efficient and effective result of K
algorithm there have been a lot of research happened
in previous day. All researchers worked on different
view and with different idea. Krishna
proposed the genetic K-means(GKA) algorithm which
integrate a genetic algorithm with K
achieve a global search and fast convergence.
Components of sensor node
Wireless sensor networks (WSNs) have gained world
wide consideration in recent years, particularly with
the proliferation in Micro-Electro
(MEMS) technology which has facilitated the
development of intelligent sensors. Sensor node as
shown in Figure 1 may also have additional
application dependent components such as a location
finding system, power generator and mobilize [1].
The analog signals produced by the sensors based on
the observed phenomenon are transformed to digital
signals by ADC, and then fed into the processing unit.
The processing unit, which is generally associated
with a small storage unit, manages the events that
make the sensor node collaborate with other nodes to
carry out the assigned sensing tasks. A transceiver
unit connects the node to the network [1]. The main
job of sensor node in a sensor field is to detect the
events, perform quick local data processing, and
broadcast the data. Energy consumption is a key issue
in wireless sensor networks (WSNs). In a sensor node,
energy is consumed by the power supply, the sensor,
the computation unit and the broadcasting unit. The
wireless sensor node, being a microelectronics device,
can only be equipped with a limited power source
(≤0.5 Ah, 1.2 V) [1].
Research and Development (IJTSRD)
www.ijtsrd.com
6 | Sep – Oct 2018
Oct 2018 Page: 1215
mean Clustering Map for Different Nodes
nd Communication Engineering
Madhya Pradesh, India
and effective result of K-mean
algorithm there have been a lot of research happened
in previous day. All researchers worked on different
view and with different idea. Krishna and Murty[4]
means(GKA) algorithm which
integrate a genetic algorithm with K-means in order to
achieve a global search and fast convergence.
Wireless sensor networks (WSNs) have gained world-
ation in recent years, particularly with
Electro-Mechanical systems
(MEMS) technology which has facilitated the
development of intelligent sensors. Sensor node as
shown in Figure 1 may also have additional
components such as a location
finding system, power generator and mobilize [1].
The analog signals produced by the sensors based on
the observed phenomenon are transformed to digital
signals by ADC, and then fed into the processing unit.
t, which is generally associated
with a small storage unit, manages the events that
make the sensor node collaborate with other nodes to
carry out the assigned sensing tasks. A transceiver
unit connects the node to the network [1]. The main
ode in a sensor field is to detect the
events, perform quick local data processing, and
broadcast the data. Energy consumption is a key issue
in wireless sensor networks (WSNs). In a sensor node,
energy is consumed by the power supply, the sensor,
utation unit and the broadcasting unit. The
wireless sensor node, being a microelectronics device,
can only be equipped with a limited power source

2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
Fig 1: Components of a sensor node [1]
Density based Clustering algorithms: Data o
categorized into core points, border points and noise
points. All the core points are connected together
based on the densities to form cluster. Arbitrary
shaped clusters are formed by various clustering
algorithms such as DBSCAN, OPTICS, DBCLAS
GDBSCAN, DENCLU and SUBCLU [3].
II. K-MEANS CLUSTERING
There are many algorithms for partition clustering
category, such as kmeans clustering (MacQueen
1967), k-medoid clustering, genetic k
algorithm (GKA), Self-Organizing Map (SOM) and
also graph-theoretical methods (CLICK, CAST) [6].
k-means is one of the simplest unsupervised
learning algorithms that solve the well
clustering problem, in the procedure follows a simple
and easy way to classify a given data set
certain number of clusters (assume k clusters) fixed
apriority. There are main idea is to define k centre’s,
one for each cluster. These centers should
a cunning way because of different location causes
different result. So results, the better choice
them as much as possible far away from each other.
For the next step is to take each point belonging to
a given data set and associate it to the nearest centre.
When no point is pending, the first step is completed
and an early group age is done.
After we have these k new cancroids, a new binding
has to be done between the same data set
points and the nearest new centre. A loop has been
generated. As a result of this loop we
that the k canters change their location step by step
until no more changes are done or in
canters do not move any more.
A. The process of k-means algorithm:
This part briefly describes the standard k
algorithm. K-means is a typical clustering algorithm
in data mining and which is widely used for clustering
large set of data’s.
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
Fig 1: Components of a sensor node [1]
Density based Clustering algorithms: Data objects are
categorized into core points, border points and noise
points. All the core points are connected together
based on the densities to form cluster. Arbitrary
shaped clusters are formed by various clustering
algorithms such as DBSCAN, OPTICS, DBCLASD,
GDBSCAN, DENCLU and SUBCLU [3].
There are many algorithms for partition clustering
category, such as kmeans clustering (MacQueen
medoid clustering, genetic k-means
Organizing Map (SOM) and
theoretical methods (CLICK, CAST) [6].
the simplest unsupervised
the well known
clustering problem, in the procedure follows a simple
to classify a given data set through a
clusters (assume k clusters) fixed
is to define k centre’s,
should be placed in
because of different location causes
choice is to place
far away from each other.
step is to take each point belonging to
given data set and associate it to the nearest centre.
the first step is completed
After we have these k new cancroids, a new binding
the same data set
the nearest new centre. A loop has been
this loop we may notice
that the k canters change their location step by step
in other words
means algorithm:
This part briefly describes the standard k-means
means is a typical clustering algorithm
in data mining and which is widely used for clustering
Fig. 2: K-Means Algorithm [5, 6]
A.1 K-mean Algorithm Process
1. Select K points as initial centroid.
2. Repeat.
3. Form k clusters by assigning all points to the
closest centroid.
4. Recomputed the centroid of each cluster Until the
centroid do not change.
III. Result Analysis of K-
k-means techniques is one of
unsupervised learning algorithms
well known clustering problem. We consider
access points of K-means technique for 150, 50 and
100 Nodes as shown in Figure 3, Figure 4, and Figure
5, respectively.
Fig. 3: K-means clustering map for 150 Nodes
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Oct 2018 Page: 1216
Algorithm [5, 6]
mean Algorithm Process
Select K points as initial centroid.
Form k clusters by assigning all points to the
Recomputed the centroid of each cluster Until the
Means Technique
one of the simplest
algorithms that solve the
known clustering problem. We consider five
means technique for 150, 50 and
Figure 3, Figure 4, and Figure
means clustering map for 150 Nodes

3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
@ IJTSRD | Available Online @ www.ijtsrd.com
Fig. 4: K-means clustering map for 50 Nodes
Fig. 5: K-means clustering map for 150 Nodes
IV. CONCLUSION
The K-means algorithm is a popular clustering
algorithm. Distance measure plays a important rule on
the performance of this algorithm. Also, we will try to
extend future this work for another partition clustering
algorithms like K-Medoids, CLARA and CLARANS.
REFERENCES
1. Vibhav Kumar Sachan, Syed Akht
“Energy-efficient Communication Methods in
Wireless Sensor Networks: A Critical Review”
International Journal of Computer Applications
(0975 – 8887) Volume 39– No.17, February 2012.
2. Shweta Bhatele and Lalita Bargadiya “Evaluation
of Communication Overhead and Energy
0 100 200 300 400 500 600 700
0
100
200
300
400
500
600
700
800
900
1000
2
3
4
7
8
10
11
14
15
16
19
21
22
24
25
26
27
28
29
30
31
32
33
35
36
38
39
40
42
43
44
46
47
48
49
50
0 100 200 300 400 500 600 700
0
100
200
300
400
500
600
700
800
900
1000
2 3
4
7
8
10 11
14
15
16
19
21
22
24
25
26
27
28
29
30
31
32
33
35
36
38
39
40
42
43
44
46
47
4849
50
51
52
53
54
55
56
57
60
61
64
66
67
68
69
70
71
72
74
75
76
77
78 79
80
83
85 87
88
90
91
92
94
95
96
97
98
99
101
102
103
104
105
106
107
109
110
111
112
114
115
116
118
119
120
121
122
124
129
130
131
132
133
135
136
137
138
139
141
142
144
145
146
147
148
149
150
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018
means clustering map for 50 Nodes
means clustering map for 150 Nodes
means algorithm is a popular clustering
algorithm. Distance measure plays a important rule on
the performance of this algorithm. Also, we will try to
extend future this work for another partition clustering
Medoids, CLARA and CLARANS.
Vibhav Kumar Sachan, Syed Akhtar Imam
efficient Communication Methods in
Wireless Sensor Networks: A Critical Review”
International Journal of Computer Applications
No.17, February 2012.
Shweta Bhatele and Lalita Bargadiya “Evaluation
Overhead and Energy
Consumption in Wireless Sensor Network using
Different Clustering Techniques” IJSHRE, Vol.
2(1), 2014.
3. T. Mohana Priya, Dr. A.Saradh “An Improved K
means Cluster algorithm using Map Reduce
Techniques to mining of inter and intra cluster
datain Big Data analytics” International Journal of
Pure and Applied Mathematics, Volume 119 No.
7 2018, pp. 679-690.
4. Nidhi Singh, Divakar Singh “Performance
Evaluation of K-Means and Heirarichal Clustering
in Terms of Accuracy and Running Time”
International Journal of Computer Science and
Information Technologies, Vol. 3 (3) , 2012, pp.
4119-4121
5. Dibya Jyoti Bora, Dr. Anil Kumar Gupta “Effect
of Different Distance Measures on the
Performance of K-Means Algorithm: An
Experimental Study in Matlab” Dibya Jy
et al, / (IJCSIT) International Journal of Computer
Science and Information Technologies, Vol. 5 (2)
, 2014, pp. 2501-2506.
6. Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza
Sattar & M.Inayat Khan, “Data Mining Model for
Higher Education System “,Europe
Scientific Research, ISSN 1450
No.1 ,2010, pp.27.
7. Amrinder Kaur, “Simulation of Low Energy
Adaptive Clustering Hierarchy Protocol for
Wireless Sensor Network”, Volume 3, Issue 7,
July 2013.
8. Amitabh Basu , Jie Gao, Joseph S. B. Mit
Girishkumar Sabhnani , “Distributed localization
using noisy distance and angle information” 7th
AC Minternational symposium on Mobile ad hoc
networking and computing, 2006,pp. 262
9. Rui Zhang, Myung J.
“Mobile sink update local information through
these Aps”, 2008.
10. Shiv Prasad Kori, “Performance Comparison in
Terms of Communication Overhead for Wireless
Sensor Network Based on Clustering Technique”,
Volume 4, Issue 3, ISSN (Online): 2249
ISSN (Print): 2278–4209.
11. Labisha R. V, Baburaj E, “Energy Efficient
Clustering Algorithms in Wireless Sensor
Networks-An Analytical View”, Volume9,
Number3, May 2014.
12. E. Ekici , S. Vural, J. McNair, D. Al
probabilistic location verification in randomly
deployed wireless sensor networks “ Elsevier
Science Publishers B. V, 2006 ,pp.,195
800 900 1000
1
5
6
9
12
13
14
17
18
20
23
32
34
37
41
45
800 900 1000
1
5
6
9
12 13
14
17
18
20
2332
34
37
41
45
58
59
62
63
65
7381
82
84
86
89
90
93
100
108
113
117
123
125
126
127
128
134
135
137
140
143
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Oct 2018 Page: 1217
Consumption in Wireless Sensor Network using
Different Clustering Techniques” IJSHRE, Vol.
A.Saradh “An Improved K-
means Cluster algorithm using Map Reduce
Techniques to mining of inter and intra cluster
datain Big Data analytics” International Journal of
Pure and Applied Mathematics, Volume 119 No.
Nidhi Singh, Divakar Singh “Performance
Means and Heirarichal Clustering
in Terms of Accuracy and Running Time”
onal Journal of Computer Science and
Information Technologies, Vol. 3 (3) , 2012, pp.
Dibya Jyoti Bora, Dr. Anil Kumar Gupta “Effect
of Different Distance Measures on the
Means Algorithm: An
Experimental Study in Matlab” Dibya Jyoti Bora
et al, / (IJCSIT) International Journal of Computer
Science and Information Technologies, Vol. 5 (2)
Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza
Sattar & M.Inayat Khan, “Data Mining Model for
Higher Education System “,European Journal of
Scientific Research, ISSN 1450-216X Vol.43
Amrinder Kaur, “Simulation of Low Energy
Adaptive Clustering Hierarchy Protocol for
Wireless Sensor Network”, Volume 3, Issue 7,
Amitabh Basu , Jie Gao, Joseph S. B. Mitchell,
Girishkumar Sabhnani , “Distributed localization
using noisy distance and angle information” 7th
AC Minternational symposium on Mobile ad hoc
networking and computing, 2006,pp. 262 – 273.
Lee, Seong-Soon Joo,
local information through
Performance Comparison in
Terms of Communication Overhead for Wireless
Sensor Network Based on Clustering Technique”,
Volume 4, Issue 3, ISSN (Online): 2249–071X,
V, Baburaj E, “Energy Efficient
Clustering Algorithms in Wireless Sensor
An Analytical View”, Volume9,
E. Ekici , S. Vural, J. McNair, D. Al-Abri “Secure
probabilistic location verification in randomly
ss sensor networks “ Elsevier
Science Publishers B. V, 2006 ,pp.,195-209