Enhancing Clustering Techniques in Wireless Sensor Networks for Improved Performance

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 8 Issue 1, January-February 2024 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD61342 | Volume – 8 | Issue – 1 | Jan-Feb 2024 Page 119
Enhancing Clustering Techniques in Wireless
Sensor Networks for Improved Performance
Dilip Kumar Sharma, Jasmeet Singh Tuteja
Department of Electronics and Communication Engineering,
Ujjain Engineering College, Ujjain, Madhya Pradesh, India
ABSTRACT
A wireless sensor network is a group of specialized transducers with
a communications infrastructure for monitoring and recording
conditions at diverse locations. Commonly monitored parameters are
humidity, pressure, temperature, wind direction and speed, vibration
intensity, illumination intensity, sound intensity, chemical
concentrations, power-line voltage, pollutant levels and vital body
functions. A wireless network communication become a common for
any data and IEEE 802.11 is a used for wireless LAN communication
because of its simplicity system. Wireless sensor node deployment
deferent topology are frequently changed are for the using sensor
node. In this paper analysis and improve energy consumption for
using different node 50, 100 and 150 using K-mean, fuzzy and SOM
algorithm with five cluster and as a result to increase the network life
time, using fuzzy logic inference system using Matlab R2013a
Simulation tools.
KEYWORDS: Wireless sensor network, SOM algorithm, K-mean,
Fuzzy, Sensor Network
How to cite this paper: Dilip Kumar
Sharma | Jasmeet Singh Tuteja
"Enhancing Clustering Techniques in
Wireless Sensor Networks for Improved
Performance" Published in International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN:
2456-6470,
Volume-8 | Issue-1,
February 2024,
pp.119-123, URL:
www.ijtsrd.com/papers/ijtsrd61342.pdf
Copyright © 2024 by author (s) and
International Journal of Trend in
Scientific Research and Development
Journal. This is an
Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
1. INTRODUCTION
A Sensor is a device that responds and detects some
type of input from both the physical or environmental
conditions, such as heat, light, pressure etc. The
output of the sensor is generally an electrical signal
that is transmitted to a controller for further
processing. A wireless sensor network consists of
autonomous sensors scattered in an environment
where they monitor conditions such as temperature,
sound, and pressure. Because of the huge size of this
forest, changes in the forest affect not only the local
environment but also global climate by altering wind
and ocean current patterns [5, 6, 7]. WSN include
wireless sensor networks applications such as
wireless, Zigbee, home automation, SCADA
transformer health monitoring system and so on.
Fig. 1: Sensor Network (Source and Storage
Node)
Depending on the environment, the types of networks
are decided so that those can be deployed underwater,
underground, on land, and so on.
2. ENERGY CONSUMPTION
Wireless Sensor Network (WSN) plays an extremely
significant role in usual lives. Wireless Networks in
IJTSRD61342

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
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 [5].
A. Cluster Head
Clustering is used in order to advance the scalability
of network performance. Clustering is useful in
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 [9].
B. Sensor Nodes Architecture
A sensor network consists of multiple detection
stations called sensor nodes, lightweight and portable
system, in each of which is small node. The every
sensor node is equipped with a transducer,
transceiver, power source, and microcomputer. The
transducer generates electrical signals based on
sensed physical phenomena and effects. The
microcomputer processes and stores the sensor output
data. The transceiver commands from a central
computer and transmits data to that computer system.
The power for each sensor node is derived from a
battery [10-12].
Fig. 2: Sensor Nodes Architecture
3. 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 following Simulation Performance
Parameter below:
Table 1: Simulation parameter in WSN
S. No. Parameter Value
1. Clustering technique K-Means, Fuzzy and SOM
2. No. of node 150, 100 and 50
3. No. of cluster 5
4. Update time 10 sec
5. Update distance 50 m.
6. Sink velocity 50-300 m/s.
7. Network length 1000 × 1000 m2
A. Energy consumption
Energy consumption is easily one of the most fundamental but crucial factor determining the success of the
deployment of sensors and wireless sensor networks (WSNs) due to many severe constraints such as the size of
sensors, the unavailability of a power source, and inaccessibility of the location and hence no further handling of
sensor devices once they are deployed.

50 100 150 200 250 300
1
2
3
4
5
6
7
8
Velocity in m/s
%
decay
rate
of
energy
for
WSN
Energy consumption in WSN with diffrent velocity for cluster based protocol
K-means
Fuzzy
SOM
Fig. 3: Performance of Energy consumption in WSN for 50 Nodes
The generic goal here is to reduce the amount of energy consumption of some components of the application as
much as possible by reducing the tasks that have to be performed by the sensors and the associated networks yet
still fulfil the goal of the intended application. We consider 5 access points with different clustering techniques
K-means, Fuzzy and SOM for 50, 100 and 150 Nodes as shown in figure 3, 4, and 5 respectively and result
analysis shown in table 2. Fig. 6 shown the performance for Energy consumption using K-means, Fuzzy, SOM.
50 100 150 200 250 300
1
2
3
4
5
6
7
8
Velocity in m/s
%
decay
rate
of
energy
for
WSN
Fuzzy
K-means
SOM

50 100 150 200 250 300
1
2
3
4
5
6
7
8
Velocity in m/s
%
decay
rate
of
energy
for
WSN
Fuzzy
K-means
SOM
Table 2: Simulation result performance for Energy consumption
No. of node Velocity in m/s
Average Energy consumption
K-means Fuzzy SOM
50
100 3.285% 2.304% 2.315%
200 5.694% 4.309% 3.982%
300 7.566% 5.872% 5.457%
100
100 1.768% 2.304% 2.315%
200 4.411% 4.309% 3.982%
300 7.226% 5.872% 5.457%
150
100 1.882% 2.392% 2.337%
200 4.269% 4.227% 4.02%
300 7.221% 5.784% 5.507%
Fig. 6: Performance for Energy consumption using K-means, Fuzzy, SOM
4. RESULT ANALYSIS
In the simulation result we consider main approaches
Clustering technique such as SOM, Fuzzy and K-
Means, with different numbers of node like 50, 100
and 150 and five clusters. The simulation
performance analysis is based on comparison of
energy consumption. On the consider at 50 node the
minimum consumption on SOM as compared to K-
mean and fuzzy logy, we also analysis on the 100 and
150 nodes are also calculated minimum energy
consumption as compared to other cluster techniques.

5. CONCLUSION
In the simulation result we consider main approaches
Clustering technique such as SOM, Fuzzy and K-
Means, with different numbers of node like 50, 100
and 150 and five clusters. The simulation
performance analysis is based on comparison of
energy consumption. On the consider at 50 node the
minimum consumption on SOM as compared to K-
mean and fuzzy logy, we also analysis on the 100 and
150 nodes are also calculated minimum energy
consumption as compared to other cluster techniques.
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