Design and Implementation a New Energy Efficient Clustering Algorithm Using the Fuzzy Logic and Genetic Algorithm for Wireless Sensor Networks

International Journal of Mobile Network Communications & Telematics (IJMNCT) Vol. 6, No.2, April 2016
DOI : 10.5121/ijmnct.2016.6201 1
DESIGN AND IMPLEMENTATION A NEW ENERGY
EFFICIENT CLUSTERING ALGORITHM USING THE
FUZZY LOGIC AND GENETIC ALGORITHM FOR
WIRELESS SENSOR NETWORKS
Sayyed Hedayat Tarighinejad 1,2
and Reza Alinaghian 3
and Mehdi Sadeghzadeh4
1
Faculty of Computer Engineering,Najafabad Branch,Islamic Azad
University,Najafabad,Iran
2
Faculty of Computer Engineering,Najafabad Branch,Islamic Azad
University,Najafabad,Iran
3
Department of computer, Mobarakeh Branch, Islamic Azad
University,Mobarakeh,Isfahan,Iran.
4
Department of computer,Mahshahr Branch, Islamic Azad University,Mahshahr,Iran
ABSTRACT
Wireless Sensor Networks are consist of small battery powered devices with limited energy resources.Once
deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy
source is not feasible. Hence, one of the most important issues that need to be enhanced in order to
improve the life span of the network is energy efficiency. to overcome this demerit many research have
been done. The clustering is the one of the representative approaches. In this paper, we introduce a
dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design
and system optimization by genetic algorithm is presented approach to select the best cluster head in
sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented
in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in
comparison with LEACH method is calculated in select the cluster head. The results of evaluations is
representative of a reduction the MSE metric in proposed method in comparison with LEACH method for
select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless
sensor network and can cause the reduce energy consumption and increase network lifetime.
KEYWORD
Wireless sensor networks, fuzzy logic, fuzzy system, genetic algorithm, energy consumption, clustering.
1. INTRODUCTION
With the advent and development of microelectronic technology in the late 70s, the new sensors
were considered. Using microelectronic technology, were produced the low-cost sensors with
small dimensions and low weight. New raw materials for fabrication of the sensor is discovered,
and subsequently considered the new principles for practical purposes and data collection. The
integration of sensor and electronic circuitry of signal anamorphic is created significant
opportunities for the majority of applications. Today, reduce the size and weight of the sensors
and increases their susceptibility is the main goal of many research laboratories and different
companies. The small size of sensor nodes was sense of smaller their energy productive batteries.
The main reason for the emergence and development of wireless sensor networks has been
applications of continuous monitoring environments that achieve and permanent human presence

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in them is difficult or impossible. As a result, generally recharging or replacing the dead nodes
(disabled due to the finish of the energy source) is not possible, because as was said these nodes
usually placed in the environments and hard, rough and inaccessible conditions and often become
scatter randomly and appropriately in environment. Thus, two points are important in the
performance of sensor networks: one is the lifetime and another is network coverage of these
networks. Since monitoring applications are typically time-consuming, it is expected that the
lifetime of sensor networks to be long enough. But if we total space the network divide into
virtual areas, usually in each area there are several sensor node. Thus, in case of death some of
nodes a area, other nodes can be partially maintain the network coverage. But if die all nodes of
placed in the area of network, virtually is impossible the area monitoring and called will
disappear the network coverage. Therefore, random and scattered death of sensor nodes, is better
than aggregation the dead nodes in the area. Therefore, the solution that considered for achieve to
increase lifetime and maintain the network coverage in sensor networks, is reduce energy
consumption of nodes, simultaneous with energy uniform consumption in all nodes in the
network. With respect to these reasons, the main purpose of sensor networks is wise and rational
management of energy resources. Therefore, it is first necessary to understand the resources of
energy consumption as well. Some of the energy consumptions of sensor node may be useful,
such as 1-sending or receiving data 2-processing queries of requested 3-sending queries and data
to neighbor nodes. But, use of high and idle of energy in sensor node related to: 1.passive
listening: that is listening to the inactive channel for a possible traffic receiving. 2. Collision
(clash): that is when a node receive more than one package at a time, even if receive two
packages are only partially synchronic. All package that are cause clash, must be discarded and
were de novo sending, that the retransmission cause increase energy consumption. 3.
Eavesdropping: that is a node the receive the packages that are sent to other nodes.4.overhead of
control packages: thus, the minimum control packages should be used for sending data.5.send a
message while the destination node is not ready to receive[1]. It is necessary to manage the
energy consumption that we know of energy consumption level of different of the sensor node.
The following points are noteworthy about the energy consumption of various parts a sensor
node, that have important role in the application of the reduce energy consumption. As shown in
figure1, the communication subsystem than the computing subsystem has a much higher energy
consumption. It is proven that send a bit of information can consumption the energy in extent the
execution of thousands of instructions. Therefore, a compromise must be establish between the
communication and the processing.
Figure1. comparison of energy consumption in different parts of the sensor node.

3
Energy consumption in conditions of reception, transmission and idle is almost the same size
(figure1). At the same time, energy consumption of radio below fall at least one order of
magnitude in sleep mode. Therefore, the radio should whenever possible stand in sleep (idle)
mode.
According to the specific application, sensory subsystem may be a significant source of energy
consumption. Therefore, energy consumption must be reduction. The most important problem
this is that to minimize the energy consumption of sensor nodes, at the same time we meet the
needs of the application. Today, the power dynamic management techniques that attention to the
reduce energy consumption of sensor networks after their design and placement, had been
addressed the greatest attention and have received many research studies around the world. Also,
in recent years, the possibility of using intelligent tools such as neural networks has been proved
in ways to the increase energy efficiency in wireless sensor networks.
Despite accomplished advances in this type of networks, sensor nodes due to the large number,
small size and placement of the contingent, still to meet its energy are based on batteries with low
power. Also, due to the use of such networks in harsh environments and inaccessible, there is on
possibility of recharging or replacing the sensor nodes. Therefore, one of the most important
issues in wireless sensor networks is problem of a server energy limitation. Also, since the sensor
network efficiency is depend heavily on network lifetime and its network coverage, so in terms of
energy storage algorithms in design of sensor networks with long life is vital. In recent years was
created the strong tendencies to use artificial intelligence techniques to the reduce energy
consumption in wireless sensor network. For example, artificial neural networks due to the
possess of convenient features such as automatic classification of data the reduction of data
dimensions, easy processing almost in all aspects of the reduce energy consumption in wireless
sensor networks are used as effective and powerful tool[2]. As shown in figure1, communications
in wireless sensor networks are one of most use the energy consumption cases. In this paper with
studies conducted for problem of cluster-head selection in wireless sensor networks and using the
uncertainties in the properties of each node at different times, we have proceed for fuzzy system
design[3]to select the best cluster-head in every moment. For the proposed fuzzy system design
and optimization of provided fuzzy system parameters, was used genetic optimization algorithm
4]. The proposed fuzzy system is evaluated on the designed data set to evaluate of energy
consumption level in sensor networks and the results is calculated to determine the reduce energy
consumption in the proposed method. The obtained results of cluster-head selection in wireless
sensor networks using proposed fuzzy system in comparison with cluster-head selection method
of LEACH, is representative of the reduce energy consumption in the proposed method of study.
Reduce energy consumption in wireless sensor networks have effective directly on the
performance and the increase lifetime of networks and can be considered an important step to
achieve QOS in sensor networks. In the second part of this article has been addressed to review
new recent studies of carried out in the cluster-head selection for wireless sensor network and
also carried out studies to design of fuzzy systems using innovative algorithms and particular the
genetic algorithm. In the third part is described the fuzzy system and in part IV has been
addressed to examine the data set of used in the evaluation and also analysis of the obtained
results to determine the reduce energy consumption by using the proposed fuzzy system. In the
final part also has been addressed to present the conclusion and recommendation for more studies
in this field of research.
2.RELATED WORKS
For the clustering in wireless sensor networks, is presented many algorithms that in below refer to
some of them. LEACH algorithms[5] which is most known as the clustering algorithm, select the
cluster head node by a random process. The algorithm the act fully distributed and when a node

4
the selected as a cluster head node, all nodes with select of the closest node to the cluster head
node the placement in formed clustered. In this algorithm may in a period unselected any cluster
head or to be select many number of cluster head. Another problem that there are in this method
is that may all the cluster head to be select in the angle of network. The most important problem
of LEACH is switching overhead of clusters and energy dissipation versus other algorithms. In
algorithm of LEACH-C[6]which is an improved LEACH, each node the sent the location and the
amount of remaining energy to SINK then sink, nodes of the cluster head the choose by a central
algorithm. Furthermore in order to better understand the proposed methods for clustering and
selection of cluster head in wireless sensor networks has been addressed to classify these methods
in table 1.
Table(1): the most important proposed methods for clustering and selection of cluster head in
wireless sensor networks.
Table(1)
MethodProposed methodDisadvantagesyear
LEACH [6]
Cluster head node the
selection in form of a
random process.
Substitution overhead of
clusters and loss energy
2002
LEACH-C
[6]
SINK the select to
cluster head nodes by
central algorithm
Is very sensitive and
dependent on SINK
position
2002
PEBECES[7]
Divide the network
into several parts with
equal size and then
classification these
parts to clusters with
different sizes
Clusters are fixed and
have not very application
dynamic sensor networks
2009
ESC [8]
Use of multi-level
clustering
The increase of
information dispatch rate
2009
Then, according to the concepts of artificial intelligence such as fuzzy and genetic system in
proposed method, using intelligent ways has been addressed to review the most important carried
out studies for clustering and selection of cluster head in wireless sensor networks. For example,
shiyuan jin using genetic algorithm, dynamically select the clusters and cluster heads, but in this
method, fitness function only is considered the distance between the nodes and cluster head
number and regardless of the remaining. But since the cluster heads the consumption the amount
of more energy than conventional nodes, so one of the main parameters for the selection of the
cluster heads is the amount energy remaining of sensors.
In table(2) using the concepts of artificial intelligence has been addressed to the most important
proposed methods for clustering and selection of cluster head in wireless sensor networks and
disadvantages of each of these methods are also investigated.
Table (2) : the most important proposed methods for clustering and selection of cluster head in
wireless sensor networks using the concepts of artificial intelligence.

5
Table(2)
MethodProposed methoddisadvantagesyear
Shiyuan Jin[9]
Using genetic algorithm,
clusters and cluster heads
the selected dynamically
Only is
considered
distance
between nodes
and number of
cluster head and
regardless of
residual
2008
Gupta [10]
Is used fuzzy logic for
find cluster heads
In each period
be select only a
cluster head
2005
LCA[11]
LCA the select a node as
cluster head that have
more characteristic than
neighbor cluster head
The creation of
the less number
of cluster
2002
Fuzzy logic is presented for the first time in 1965 by professor Zadeh and so far have been great
advances[12]. Of the most important studies carried out for fuzzy logic can refer to present of
fuzzy system by Mamdani and sugeno[13]. In order to design the optimal fuzzy system also was
carried out studies among [14] and [15], that in each of them using innovative algorithms such as
genetic and particle swarm optimization algorithm has been addressed to offer of methods for
design the optimal parameters in the fuzzy system.
3. PRESENTATION OF THE PROPOSED METHOD
In this section, by review of proposed methods to discuss challenges in this study and with the use
of fuzzy system we have to choose the best node of cluster head in sensor network.
In 1-3 and 2-3 sections due to the use of fuzzy logic and genetic algorithm, in proposed method of
paper has been addressed to study the basic concepts. In section 3-3, using LEACH algorithm has
been addressed to examine of how to choose the cluster head in sensor networks, and in section 4-
3 has been addressed to description of the proposed method to reduce energy consumption in
wireless sensor networks.
3-1. fuzzy logic
Uncertainty has been always one of the challenges of very important in different science. In
recent years, researchers using study and recognition of uncertainty, the provide methods and
models for the implementation of uncertainty in engineering sciences that result of them is offer
of concepts such as fuzzy logic. Including the most modern advances in engineering systems is
presentation and implementation of fuzzy system for control process. In fuzzy system that with
two types of Mamdani and sugeno[13], using the concepts of fuzzy Logic, fuzzy inference,
absolute numbers and fuzzy set to study the environment and uncertain decision, we have to make
decision to solve problems[16.] In figure 2, has been addressed to examine the most important
components in design the fuzzy system of sugeno that used in this study[17].

6
Figure 2: study of most important components in design the fuzzy system of sugeno
The most important components used in the fuzzy system, is part of the knowledge base, that
using fuzzy rules and fuzzy logic to determine for fuzzy decision-making. To understand how
decision-making in inference engine of fuzzy system in figure (3), using fuzzy rules and terms
and linguistic variable has been addressed to graphically to evaluate how fuzzy inference[18].
Figure3: study of fuzzy inference using fuzzy rules in the system.
In the above figure using fuzzy operators of MAX-MIN has been addressed to fuzzy inference for
three rules mentioned in figure[3]. The several types of fuzzy function there are for use in fuzzy
system that with regard to problem, use of these functions. For example, membership degree in
fuzzy function of bell-shaped is like figure (4)[18].
Figure (4): membership degree in fuzzy function of bell-shaped.

7
As indicated in figure 2, for design a fuzzy system is require to several parameters including
optimal rules. For design the optimal parameters in design the fuzzy system, presented different
methods that in this study is used the genetic algorithm.
In next section has been addressed to a short presentation of the basic concepts of this algorithm.
3-2. Genetic algorithm
GA algorithm is a developed search method by Holland 1975[19], which is consistent based on
the mechanism of natural selection and reproduction to a random search but oriented through
decision making area for find the optimal solutions of comprehensive. Basically, GA algorithms
of system to provide feasible solutions for the optimization need that usually feasible solution to
be define by a linear chain called chromosome that its length is different in terms of the type
application. In GA systems occur three basic functions: reproduction, exchange selection and
mutation. Reproduction (proliferation) is process in which chains in terms of weight factor that is
depend on optimal of solutions of chain (fitness function) were reproduce.
Exchange is process in which new chain randomly combined together and each chain pair
(parent) the exchange part of its information and the creation a new chain pair (child). Mutation is
process which by it alteration the amount of one of chain rings randomly. Each chain ring (box) is
an indicator of gen. Number above the box is indicator of activity status in the network (activity
number) and numbers inside the boxes are for the indicator of option number for activity.
In the process of exchange, two members randomly selection, and their chromosomal information
be exchange. Exchange is done only with exchange with p-c possibility. It is said that the power
of GA is in exchange process in which occur random exchange the genetic characteristics, that
probably a good answer can make a good answer.
In figure 2, chains of 1 and 2 randomly selection, in a random point (gene 5) has been broken,
was exchange the genetic information and shaped are two new chains.
Figure 5: exchange operation
In mutation process, change the genetic contents of some of the genes of an answer (with
probability pm). GA system so continue with reproduction, exchange and mutation process. After
several generations is achieved optimal solution. The balance between exchange and mutation is
accomplishable with possibility their occurrence. Commonly,
The following using a genetic algorithm has been addressed to describe how proposed system
design and optimization of fuzzy system rules.

8
3-3. Cluster head selection algorithm of LEACH
Clustering hierarchy protocol of adaptive with low energy is first and most well-known protocol
based on clustering in wireless sensor networks, in which occur the creation of clusters in the
form of distributed. The most important purpose of LEACH is have a local base stations (cluster
head) for reduction the energy consumption result of data transmission to a remote base station.
LEACH the chosen a small sensor node randomly as cluster head and organization the local
nodes as local clusters. Assign of nodes to respective cluster head is done based on proximity
(distance). Non-cluster head nodes (also called normal nodes), transfer the data to cluster head.
Therefore, the only overhead that there are for them, is intra-cluster communication. Cluster head
nodes than normal nodes the need to more energy. Therefore, selecting of fixed cluster head
nodes lead to early energy discharge and their premature death. The balance of energy of cluster
heads with periodic rotation the role of the cluster head is established between the different nodes.
Also, the use of data collection or composition in the cluster heads, lead to reduction the message
transfer to the base station and resulting in energy storage. LEACH protocol operation is divided
into several rounds. Each round begin with installation stage (cluster form), where clusters are
organize. Following the installation stage, there is data transfer stage in which normal nodes, send
the data to the cluster heads and cluster heads after data collection or composition process,
transfer the integrated packing into the base station until reduce the amount of data that must send
to base station. In LEACH, timing of sensor data transmission is performed by protocol of code
Division Multiple Access (CDMA) or time division multiple Access (TDMA). Select the cluster
head is done through a probability function. Each node the select a random number between zero
and one, and if the selected number is less than T(n), that node is selected as current round cluster
head(5).
Formula(1)







∈






−=
otherwise
Gnif
P
rP
P
nT
0
1
mod1)(
P is the probability of the cluster head, r is the current round number and G is set of nodes of that
in current round 1/p were not a cluster head. Based on simulation model, has been shown that
only five percent of the clusters are require to getting cluster head. The strength point of LEACH
is in the rotation mechanism the role of the cluster heads and data collection also extend the
network lifetime.
4-3. Describe the proposed method
To solve the problem of choosing the best cluster head in wireless sensor networks, in this paper
the control method is presented based on fuzzy logic. In the proposed method that in figure 6 is a
schematic of it visible, at first, all nodes to send the message. After forward of the messages, the
residual energy of each node and the distribution of each node the available of the proposed fuzzy
control. The proposed fuzzy control using the residual energy of each node and the distribution of
each node, proceed to the decision making to select the best cluster head in sensor network.
Linguistic variable represent the energy of a node in sensor networks, energy level and
distribution of a number of neighbors of a node.

9
Figure(6): schematic of application stages of the proposed method to select the best cluster head in wireless
sensor networks.
Using proposed method in this research, first, error level of transmission of messages by any of
the nodes in a sensor network the send to designed genetic algorithm and genetic algorithm using
sensing in formation will attempt to fuzzy system construction and extract the fuzzy rules. Then,
fuzzy control using the remaining energy level in each node and the distribution of each node will
attempt to the optimal cluster head and proportion with capability of each node. To fuzzy system
design used in proposed method, is necessity to the extraction of optimal parameters.Procedures
of forenamed algorithm in this paper is that at first, error level of transmission of messages by any
of the nodes in a sensor network the send to genetic algorithm as input. Then, the algorithm using
conversion of fuzzy rules into the chromosomes and algorithm genes will attempt to determine
the best rules in the proposed fuzzy system and with the production of different generations and
with the rules will be produced in each generation, will reduce the error rate in the selection the
cluster head. In figure (7) has been addressed to determine the mentioned cases for fuzzy system
optimization and fuzzy rules.
Figure7: flowchart the optimization of fuzzy rules in the proposed fuzzy system.
Among the most important parameters in the design of genetic algorithm used in this study, may
be cited to cost function. For calculate the cost function in this study, is used squared error metric
(formula3) in energy consumption of sensor networks. Then, in figure 8 is visible the overview of
proposed fuzzy system for selection the cluster head in sensor networks which is simulated in
MATLAB software.

10
Figure8: the overview of proposed fuzzy system for selection the cluster head in sensor networks.
Finally, for better understanding, a summary of the parameters used in genetic algorithm is
presented in table3.
Fitnes
s
Generati
ons
Elite
count
Crossover
fraction
Selection
Populat
ion size
Mutation
SE7528
stochastic
uniform
25
Gaussian(0.1,0.
1)
Table3: The most important parameters used in the genetic algorithm to optimize of proposed
fuzzy system rules. After the fuzzy system rules be optimization, rules designed in the inference
engine of proposed fuzzy system will be used for selection the of best cluster head in sensor
networks, that in figure 9 designed inference engine is visible for proposed fuzzy system.
Figure9: The designed inference engine for proposed fuzzy system.

11
In figure 10 has been addressed to examine how to reduce the error rate in the rules used in the
proposed fuzzy system that are designed by the genetic algorithm.
Figure10: Reduce the error rate in different generations used in the genetic algorithm.
In this example using production of 25 generations we have been able achieve to the nearest value
to zero the error rate in proposed fuzzy system rules for selection of the best cluster head in
wireless sensor networks. The reduce the error rate in the selection of the best cluster head in
wireless sensor networks will be lead to reduce energy consumption in a time period and less
energy in sensor networks is to sense of extend the lifetime of this type of network. In the next
section to evaluate the proposed method in this paper, has been addressed to present used data set
in the evaluation and as well as the presentation and analysis of the evaluation results for reduce
energy consumption in sensor networks.
4 .EVALUATION OF RESULTS
In this section, has been addressed to determine and evaluation of proposed method to reduce
In section 1-4 is presented used data set in the evaluation of research, in section 2-4 has been
addressed to describe the evaluation method and finally section 3-4 is presented analysis of
evaluation results.
1-4. Data set of evaluation
To evaluation of proposed method using MATLAB software is studied for fuzzy system
simulation. As mentioned in the previous section, to use of proposed method at first must be
available the proposed system the amounts of features of residual energy level in each and also
distribution the each node.
In conducted studies in this study, for the design of the proposed fuzzy control is need to consider
the most important factors of effect on selection the node as cluster head, that have important role
in the probability of getting cluster head a node in sensor network[20]. Other considered factor in
this study is scattering, that factor have also closely relation with the probability of getting cluster
head a node in sensor networks, and further dispersion each node is in sense of less chance that
node for creation the cluster head[20].
Using the formula of conditional probability, can calculate the probability of getting cluster head
each of the nodes based on the amount of energy and dispersion each node. Also, using ratio
creation can determine the probability of getting cluster head each node than increase or decrease
each of the factors of energy and dispersion. In this study, for gather of training data set and

12
assessment of proposed fuzzy system, we use the ratio. In the following is presented the
necessary formula for the collection of research data.
Formula(2): Z= X/Y
In formula(2), z is the probability or chance of getting cluster head each node, x is the energy of
each node and y is the dispersion of the node in sensor network. Because by increasing the
amount of energy of each node should increase the probability of getting cluster head that node,
so it has been the value of X in the numerator and because by increasing the amount of dispersion
of the each node should decrease the probability of getting cluster head that node, so it has been
the value of Y in the denominator.
To evaluate the proposed method we have attempted to design a hypothetical example of a sensor
network. In this designed example, the message the use of 4 cluster head to send messages and
the amount of messages is 400 messages, that is presented in table(4) assumptions and
characteristics of each node in the designed example
Table(4): primary data set for evaluation the proposed method.
The nodes of
sensor
network
The percent of
the residual
energy
The percent
of the
probability
of dispersion
The percent of
the probability
of getting
cluster head in
the LEACH
method
The percent of
actual fitness
of getting
cluster head
Node 10.500.152830.33
Node 20.150.20337.5
Node 30.350.201117.5
Node 40.50.402812.5
In the next section using the designed example in this section and using the proposed fuzzy
system has addressed to explain the evaluation method used to assess the reduce energy
consumption in sensor networks.
2-4.Describe the evaluation method
To evaluate the proposed method, at first using presented data set in the previous section that is
considered as input of proposed fuzzy system, has been addressed to measure the probability of
getting cluster head each of the nodes in designed example by the proposed fuzzy system. The
obtained results is visible in the table(5).
The SE is calculated from absolute value of the difference between the percent of actual fitness of
getting cluster head and the percent of the probability of getting cluster head in any way. The
equation of require for the calculation of ES in the form below.
Formula(3):
SE=| the percent of the probability of getting cluster head in any way – the percent of real fitness
of getting cluster head|

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Table (5): SE value for each selected node by proposed fuzzy system.
After extraction the percent of the probability of getting cluster head of each node in the
simulation of proposed fuzzy system, then has been addressed to evaluation of designed messages
in four nodes. To compare the probability of getting of getting cluster head in proposed fuzzy
system and LEACH method in the table (6), the message transmission is calculated using both
methods.
Table(6): the compare the message taken into account in the proposed method and LEACH method for
designed example and research evaluation
The number of actual
messages in terms of
fitness each node
The number
forwarding messages
in LEACH method
The number
forwarding messages
in proposed fuzzy
method
node
121.321121281
281321022
7044823
50112884
As is visible in the above, the deviation rate in the number of message in the proposed method in
all nodes comparison with LEACH method is less ratio the number of actual messages of require
in terms of fitness each node. In the next section using the calculation of SE value in the proposed
method and LEACH method, proposed fuzzy system has been addressed to calculate the reduce
3-4. Analysis of results
Using the obtained results from the above, in this section has been addressed to calculate the time
of forwarding messages at four nodes that are 400 messages. According to the results the table(6),
SE value in the proposed method and LEACH method for selection cluster heads is calculated in
hypothetical example of sensor network and is provided in the table(7).
Table (7): the time of forwarding messages in the proposed method in comparison with LEACH
method
SE in proposed fuzzy
methodSE in LEACH methodThe sensor network nodes
The percent of the
probability of
getting cluster head
taken into account
by proposed fuzzy
system
SE
The percent of the
probability of
dispersion
The percent of
the residual
energy
The nodes of
sensor
network
322.330.150.501
25.525.50.200.152
20.56.50.200.353
2215.50.400.54

14
6.6893.321
7452
18593
33224
131.68179total
As is observed in figure(11), according to the obtained results of the average error of forwarding
messages because of the death of nodes in the designed example to evaluate the proposed method,
in the proposed method and LEACH method is observed that the MSE rate in the proposed
method is lower than LEACH method in the selection cluster heads.
44.75
32.92
0 5 10 15 20 25 30 35 40 45 50
LEACH Method
Fuzyy Control Method
MSE
Figure (11): the MSE value in the proposed method in comparison with LEACH method for select the
cluster heads.
MSE metric reduce in the proposed method is meant to reduce the error of forwarding messages
because of less death of nodes, that the reduction finally will be a lot the amount of energy for
sensor network. The reduce energy consumption of the sensor networks in the proposed method,
in turn, increase network lifetime to select the cluster heads in the proposed method in
comparison with LEACH method.
5. CONCLUSION
Given the mentioned challenge in this study that is how to choose the best cluster heads in the
wireless sensor networks, in this study has been addressed to provide a control way based on the
use of genetic fuzzy systems. In the proposed method, the selecting cluster heads is done by
proposed genetic fuzzy system and based on the properties of each node in the moment of
forwarding messages that it may help to distribution of cluster heads in form of more balance in
sensor networks. To research evaluation, the proposed method is simulated in MATLAB and
using design of a hypothetical example in the use of sensor networks has been addressed to
determine the MSE rate or mean error of forwarding messages in the proposed method in
comparison with LEACH method to selection the best cluster heads. The results the study is
indicative of MSE metric reduction in the proposed method in comparison with LEACH method
in the selection of the cluster heads. The MSE metric reduction in the proposed method is meant
to reduce the error of forwarding messages that the reduction finally will be a lot the amount of
energy for sensor network.The reduce energy consumption of the sensor networks in the proposed
method, in turn, increase the lifetime of the network to select the cluster heads in the proposed
method in comparison with LEACH method.

15
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AUTHORS
Sayyed Hedayat Tarighinejad ,Iran, Kohgiloyeh and Boyerahmad Najaf Abad Azad
University graduate student and employeeof the Agricultural Bank Kohgiluyeh and
BoyerLocation Address Iran, Kohgiluyeh and Boyer-Ahmad, kohgiluyeh, DEHDASHT

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