![IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014112
Manuscript received January 5, 2014
Manuscript revised January 20, 2014
Fuzzy LogicApproach to Improving Stable Election Protocol for
Clustered Heterogeneous Wireless Sensor Networks
Baghouri Mostafa†
, Chakkor Saad††
and Hajraoui Abderrahmane†††
,
University of Abdelmalek Essaâdi, Faculty of Sciences, Tetouan, Morocco
Summary
The wireless sensor network is composed of a set of nodes which
energy is limited in terms of computing, storage and
communication power. In this network, a few nodes become
cluster head which causes the energetic heterogeneity of the
network, therefore the behavior of the sensor network becomes
very unstable as soon as the life of the first node is elapsed. SEP
has proposed the extension of time to network stability before the
death of the first node and the reduction of unstable time before
the death of the last node. This protocol is based on the election
of cluster head by the balance of the probabilities of the
remaining energy for each node. In this paper, we propose to
improve SEP by fuzzy logic (SEP-FL). We show by simulation
in MATLAB that the proposed method increases the stability
period and decreases the instability of the sensor network
compared with LEACH, LEACH-FL and SEP taking into
account the energy level and the distance to the base station. We
conclude by studying the parameters of heterogeneity as the
protocol proposed (SEP-FL) provides a longer interval of
stability for large values of additional energy brought by the
more powerful nodes (advanced).
Key words:
WSN, Fuzzy Logic, SEP, Energy lifetime, heterogeneous clusters.
1. Introduction
Wireless sensors network consists of small nodes limited in
terms of processing, data storage and communication
powers. These nodes are deployed in a large area to
sensing and sending their measurements data to the base
station for the purpose of operating [1]. The design of
management and communication protocols in the
applications for these networks must consider the optimal
energy consumption to extend the lifetime of the network
because the replacement of batteries incorporated in the
nodes is a very difficult operation once these devices are in
place. The important part of energy is consumed in the
communication circuit which must be minimized. Many
approaches have been developed to reduce energy
consumption and to guarantee well balanced distribution of
the energy load among nodes of the network. Most
solution proposed is the LEACH protocol using cluster
heads dynamically elected based on an optimal probability
model [2]. One of the drawbacks of this solution is that the
nodes of sensor network are equipped with the same
amount of energy (homogeneous sensor networks). In this
work, we assume that a percentage of the node population
is equipped with more energy than the rest of the nodes in
the same network (heterogeneous sensor networks). There
are many applications that require the energetic
heterogeneity of network nodes, since the lifetime of the
network is limited. Furthermore, there is a need to add
more nodes which will be equipped with more energy than
the nodes that are already in use. We suppose that the
coordinates of the sink and the dimensions of the field are
known. We also assume that the nodes are uniformly
distributed over the field and they are not mobile. Under
this model, we propose a new protocol SEP-FL improves
SEP protocol using Fuzzy Logic. In SEP, the election
probabilities of cluster head are weighted by the initial
energy of a node relative to that of other nodes in the
network. We show by simulation that SEP-FL provides a
longer stability period and a lower instability period and
increases life time of nodes. We study the effect of our
SEP-FL protocol to heterogeneity parameters capturing
energy imbalance in the network. We show that SEP-FL is
more resilient than SEP in judiciously consuming the extra
energy of advanced (more powerful) nodes. SEP-FL yields
longer stability period for higher values of extra energy.
2. Related work
The energy model for the wireless sensor network with
heterogeneous nodes and his setting is described as
follows: Assuming the case where a percentage of the
population of sensor nodes is equipped with more energy
resources than the rest of the nodes. Taking m as the
fraction of the total number of nodes n, which are equipped
with α times more energy than the others. These powerful
nodes are named advanced nodes, and the rest (1-m) x n is
named normal nodes. We assume that all nodes are
distributed uniformly over the sensor field. We consider
architecture of a sensor network that is hierarchically clus-
tered. Moreover, cluster heads are elected in each round
and as a result the load is well distributed and balanced
among the nodes of the network. The cluster head has to
report to the sink and may expend a large quantity of
energy, but this happens periodically for each node [2].
The energy model that we use in this study is illustrated in
Figure 1:](https://image.slidesharecdn.com/20140119-140723072640-phpapp01/85/Fuzzy-Logic-Approach-to-Improving-Stable-Election-Protocol-for-Clustered-Heterogeneous-Wireless-Sensor-Networks-1-320.jpg)
![IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014 113
Fig. 1 Radio energy consumption model
In order to achieve an acceptable Signal-to-Noise Ratio
(SNR) in transmitting L bits messages over a distance d,
the energy expended by the radio is given by equation (1):
(1)
Where Eelec is the energy dissipated per bit to run the
transmitter or the receiver circuit [2].
The optimal construction of clusters is very important
because it is equivalent to the setting of the optimal
probability for a node to become a cluster head. To
evaluate the performance of clustering protocols, we define
some metrics:
A. Stability Period: is the time interval from the start of
network operation until the death of the first sensor
node. We also refer to this period as “stable region.”
B. Instability Period: is the time interval from the death
of the first node until the death of the last sensor node.
We also refer to this period as “unstable region.”
The protocol SEP improves the stable region of the
clustering hierarchy process using the characteristic
parameters of heterogeneity, namely the fraction of
advanced nodes (m) and the additional energy factor
between advanced and normal nodes (α).
To increase the stable region, SEP attempts to maintain the
constraint of well balanced energy consumption. Advanced
nodes have to become cluster heads more often than the
normal nodes. The new heterogeneous setting (with
advanced and normal nodes) has no effect on the spatial
density of the network [2].
On the other hand, the total energy of the system changes.
Suppose that E0 is the initial energy of each normal sensor.
The energy of each advanced node is then E0 .(1+ α). The
total (initial) energy of the new heterogeneous setting is
equal to:
So, the total energy of the system is increased by a factor
of 1+ α.m
3. Proposed approach
This section describe the new method that we propose to
improve the stable and unstable region of SEP by
calculating the chance of each node to become cluster-head,
unlike to SEP protocol that takes this parameter to elect
CH as a random value, which causes the disadvantage of
poor balancing energy in the network. To solve this
problem, our approach (The SEP-FL Fuzzy logic approach
to improve Stable Election Protocol for clustered
heterogeneous WSN) is based on two deterministic
criteria: the distance from the base station and the residual
energy level of each node type. Our fuzzy system is
divided in two Fuzzy Inference Systems (FIS), one for the
advanced nodes and the other for the normal nodes. Each
system consists of four steps denoted: fuzzifier, inference
machine, rule base and defuzzifier, Figure 2 and 3 [5],
show the different architectures of this system:
EnergyNrm (3)
DistToBS (3)
Chance (9)
SEP-FL-Nrm
(mamdani)
9 rules
Fig. 2 SEP-FL architecture for normal node
EnergyAdv (3)
DistToBS (3)
Chance (9)
SEP-FL-Adv
(mamdani)
9 rules
Fig. 3 SEP-FL architecture for advanced node
4. Our FIS Parameters and Rules
In our proposed model, we use two parameters: energy
level and distance to the base station of each non-CHs
node. To study how much they are effecting the lifetime of
the network, and to make these parameters more flexible,
we divided each linguistic variable that we used to
represent these parameters into three levels: low, medium,
( ) ( ) ( )0 0 0. 1 . . . . 1 . . 1 .n m E n m E n E mα α− + + = + (2)](https://image.slidesharecdn.com/20140119-140723072640-phpapp01/85/Fuzzy-Logic-Approach-to-Improving-Stable-Election-Protocol-for-Clustered-Heterogeneous-Wireless-Sensor-Networks-2-320.jpg)
![IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014114
and high for energy level and Close, medium, and far for
the distance to the BS.
Moreover, many types of membership functions are
available in the MATLAB Fuzzy Logic toolbox [5].
However, we used the Triangle and Trapezoidal
membership functions because their degree is more easily
determined [6]. Therefore, we chose to use them to
present our parameters as illustrated in Figures 4, 5, and 6.
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0
0.2
0.4
0.6
0.8
1
EnergyNrm
Degreeofmembership
Low Medium High
Fig. 4 Fuzzy set of Energy level
0 10 20 30 40 50 60 70
0
0.2
0.4
0.6
0.8
1
DistToBS
Degreeofmembership
Close Medium Far
Fig. 5 Fuzzy set of distance to BS
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
Chance
Degreeofmembership
V-w weak L-weak L-m Medium H-m L-S Strong V-S
Fig. 6 Fuzzy set of chance value
To determine the maximum values for our parameters in
our FIS model, we have used equations (3), and (4):
max energy initialenergy= (3)
( ) ( )
22
max tan x ydis ceToBS BS BS= + (4)
Where, ( ),x yBS BS is the position of the Base Station on x
and y axis respectively.
Since we have two parameters, each divided into three
levels, we have 32
=9 possible chance values shows in
Table 1 below that represents our fuzzy IF-THEN rules.
Table 1 : Fuzzy Inference System IF-THEN rules
Energy level Distance to the BS Chance to become CH
Low Far Very weak
Low Medium Weak
Low Close Litter weak
Medium Far Litter medium
Medium Medium Medium
Medium Close High medium
High Far Litter strong
High Medium Strong
High Close Very strong
5. Determination of Cluster-Head Chance
Value
To obtain the Chance value, we aggregate the results of
each rule. This process is called defuzzification. One of the
most popular defuzzification methods is the centroid,
which returns the centre of the area under the fuzzy set
obtained aggregating conclusions. We use formula (5) to
get the value of Chance for i node in r round:
( )
( )
( )
1
1
,
n
j j
j
n
j
j
u x x
Chance i r
u x
=
=
=
∑
∑
(5)
Where u(xj) , is a membership function degree of set j, and
xj is the output chance value on x-axis that intersect with
u(xj).
6. Our algorithm protocol
In every round, sensor node (advanced and normal)
calculates the chance to become the cluster head using IF-
THEN rules which are described in precedent section.
After, it selects the maximum of these chances. If the
maximum is less than the threshold T(s) (for advanced and
for normal nodes) then the node becomes a cluster head
and advertises this fact to other nodes around the cluster.
The nodes that receive this message calculate the distance
between the cluster head and itself and send a join–](https://image.slidesharecdn.com/20140119-140723072640-phpapp01/85/Fuzzy-Logic-Approach-to-Improving-Stable-Election-Protocol-for-Clustered-Heterogeneous-Wireless-Sensor-Networks-3-320.jpg)
![IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014 115
message to the closest one of the cluster head to form a
cluster. Equations (6) and (7) define the T(s) of different
type of nodes, where Padv, Pnrm, are the probabilities to
become cluster head for advanced and normal nodes
respectively and r is current round. The G’ and G’’ are the
sets of advanced and normal nodes that not elected as
cluster heads in last 1/Padv and 1/Pnrm rounds per epoch
respectively [2].
( )
'
1
1 mod
0
adv
adv
advadv
adv
P
if S G
P rT S
P
otherwise
∈ −=
(6)
( )
''
1
1 mod
0
nrm
nrm
nrmnrm
nrm
P
if S G
P rT S
P
otherwise
∈ −=
(7)
7. Simulation and evaluation
In this section, we evaluate the performance of our
approach in MATLAB [5] in two different scenarios: α=1
and α=3 with 100 nodes are randomly distributed in a
100×100 m2
network. The initial energy of the sensors is
E0=0.5 J. The simulation was performed for 10000 rounds.
We use a simplified model showed in figure 1 for the radio
hardware energy dissipation. We compare our approach to
LEACH [3], LEACH-FL [4] and SEP [2] in Lifetime and
Network’s conception energy.
A. Network’s lifetime
Although various definitions have been proposed in the
literature, in this paper lifetime is considered as the time
when the first node dies. Figure 7 and 9 illustrates the
number of alive nodes with respect to the operation of the
network in 10000 rounds for different scenarios. It is easy
to find out that the proposed approach prolongs the
lifetime of the stability period for normal and advanced
sensors compared with other algorithms. Our proposed
algorithm improves the overall network lifetime about
67.36% and 54 % compared to SEP algorithm results for
α=1 and α=3 respectively.
B. Consumption energy in the network
Consumption energy in the network in each round can be a
good metric to measure the energy efficiency of the
algorithm.
Figure 9 and 10 show the comparison of energy
consumption rate of the four algorithms. In the proposed
approach the consumption energy of network is less than
others. This can be interpreted by the fact that our
approach offers a considerable lifetime compared to other
algorithms.
On the other hand, we note that when the value of α
increases, the lifetime of the network increases also. We
can say that the doping network energy leads to an increase
in its operating time.
0 500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
100
Rounds
AliveSensors
LEACH
LEACH-FL
SEP
SEP-FL
Fig. 7 Network Lifetime (scenarios 1 with α=3)
0 500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
Rounds
ConsumptionEnergyintheNetworkAlive
LEACH-FL
SEP
SEP-FL
LEACH
Fig. 8 Consumption energy of network (scenarios 1 with α=3)
0 500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
100
Rounds
Alivesensors
LEACH
LEACH-FL
SEP
SEP-FL
Fig. 9 Network Lifetime (scenarios 2 with α=1)](https://image.slidesharecdn.com/20140119-140723072640-phpapp01/85/Fuzzy-Logic-Approach-to-Improving-Stable-Election-Protocol-for-Clustered-Heterogeneous-Wireless-Sensor-Networks-4-320.jpg)
![IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014116
Fig. 10 Consumption energy of network (scenarios 2 with α=1)
8. Conclusion
Optimizing the energy consumption of heterogeneous
wireless sensor network can be realized by using the fuzzy
logic. Indeed, our approach shows, by simulation, the
improvement of the stability period of the network before
the death of the first node since it has two intervals of
stability on the first normal nodes, the second related to
advanced nodes that increases the lifetime and reduces the
consumption of the energy stored in each node.
SEP-FL is more energy efficient in prolonging
the network life time using two parameters
election fuzzy logic of heterogeneity in networks to
evaluate the chance of sensors to become cluster head.
As perspective of this work, we propose to simulate the
protocol SEP-FL with a robust software simulation
wireless sensor network and the realization of a prototype
of a node joining our protocol.
References
[1] F. Akyildiz, W. Su, Y. Sankarasubramaniam and al., “A
survey on sensor networks,” IEEE Communications
Magazine, vol. 40, no. 8, pp. 102-114, 2002.
[2] G. SMARAGDAKIS, I. MATTA , A. BESTAVROS and
al., “ SEP: A Stable Election Protocol for clustered
heterogeneous wireless sensor networks ” , Computer
Science Department, Boston University.
[3] W. Heinzelman, A. Chandrakasan and H. Balakrishnan,
“Energy-Efficient Communication Protocol for Wireless
Microsensor Networks,” In Proceedings of the 33rd
Hawaii
International Conference on System Sciences (HICSS '00),
January 2000.
[4] G. Ran and al., “Improving on LEACH Protocol of Wireless
Sensor Networks Using Fuzzy Logic”, Journal of
Information & Computational Science 7: 3 767–775 (2010)
[5] http://www.mathworks.com/ Fuzzy Logic Toolbox user’s
guide 2012
[6] N. A. Torghabeh and al., “Cluster Head Selection using a
Two-Level Fuzzy Logic in Wireless Sensor Networks”, 2nd
International Conference on Computer Engineering and
Technology IEEE Vol. 2 August 07 (2010)
Baghouri Mostafa is an PhD student in the
Laboratory of Systems Modeling and
Analysis, Team: Communication Systems,
Faculty of sciences, University of
Abdelmalek Essaâdi, Tetouan Morocco, his
research area is: Optimization of energy in
the wireless sensors networks. He obtained a
Master's degree in Electrical and Computer
Engineering from the Faculty of Science and Technology of
Tangier in Morocco in 2002. He graduated enabling teaching
computer science for secondary qualifying school in 2004. In
2006, he graduated from DESA in Automatics and information
processing at the same faculty. He work teacher of computer
science in the high school.
Chakkor Saad is an PhD student in the
Laboratory of Systems Modeling and
Analysis, Team: Communication Systems,
Faculty of sciences, University of
Abdelmalek Essaâdi, Tetouan Morocco, his
research area is: intelligent sensors and its
applications. He obtained a Master's degree
in Electrical and Computer Engineering from
the Faculty of Science and Technology of Tangier in Morocco in
2002. He graduated enabling teaching computer science for
secondary qualifying school in 2003. In 2006, he graduated from
DESA in Automatics and information processing at the same
faculty. He work teacher of computer science in the high school.](https://image.slidesharecdn.com/20140119-140723072640-phpapp01/85/Fuzzy-Logic-Approach-to-Improving-Stable-Election-Protocol-for-Clustered-Heterogeneous-Wireless-Sensor-Networks-5-320.jpg)
Fuzzy Logic Approach to Improving Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks
- 1. IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014112 Manuscript received January 5, 2014 Manuscript revised January 20, 2014 Fuzzy LogicApproach to Improving Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks Baghouri Mostafa† , Chakkor Saad†† and Hajraoui Abderrahmane††† , University of Abdelmalek Essaâdi, Faculty of Sciences, Tetouan, Morocco Summary The wireless sensor network is composed of a set of nodes which energy is limited in terms of computing, storage and communication power. In this network, a few nodes become cluster head which causes the energetic heterogeneity of the network, therefore the behavior of the sensor network becomes very unstable as soon as the life of the first node is elapsed. SEP has proposed the extension of time to network stability before the death of the first node and the reduction of unstable time before the death of the last node. This protocol is based on the election of cluster head by the balance of the probabilities of the remaining energy for each node. In this paper, we propose to improve SEP by fuzzy logic (SEP-FL). We show by simulation in MATLAB that the proposed method increases the stability period and decreases the instability of the sensor network compared with LEACH, LEACH-FL and SEP taking into account the energy level and the distance to the base station. We conclude by studying the parameters of heterogeneity as the protocol proposed (SEP-FL) provides a longer interval of stability for large values of additional energy brought by the more powerful nodes (advanced). Key words: WSN, Fuzzy Logic, SEP, Energy lifetime, heterogeneous clusters. 1. Introduction Wireless sensors network consists of small nodes limited in terms of processing, data storage and communication powers. These nodes are deployed in a large area to sensing and sending their measurements data to the base station for the purpose of operating [1]. The design of management and communication protocols in the applications for these networks must consider the optimal energy consumption to extend the lifetime of the network because the replacement of batteries incorporated in the nodes is a very difficult operation once these devices are in place. The important part of energy is consumed in the communication circuit which must be minimized. Many approaches have been developed to reduce energy consumption and to guarantee well balanced distribution of the energy load among nodes of the network. Most solution proposed is the LEACH protocol using cluster heads dynamically elected based on an optimal probability model [2]. One of the drawbacks of this solution is that the nodes of sensor network are equipped with the same amount of energy (homogeneous sensor networks). In this work, we assume that a percentage of the node population is equipped with more energy than the rest of the nodes in the same network (heterogeneous sensor networks). There are many applications that require the energetic heterogeneity of network nodes, since the lifetime of the network is limited. Furthermore, there is a need to add more nodes which will be equipped with more energy than the nodes that are already in use. We suppose that the coordinates of the sink and the dimensions of the field are known. We also assume that the nodes are uniformly distributed over the field and they are not mobile. Under this model, we propose a new protocol SEP-FL improves SEP protocol using Fuzzy Logic. In SEP, the election probabilities of cluster head are weighted by the initial energy of a node relative to that of other nodes in the network. We show by simulation that SEP-FL provides a longer stability period and a lower instability period and increases life time of nodes. We study the effect of our SEP-FL protocol to heterogeneity parameters capturing energy imbalance in the network. We show that SEP-FL is more resilient than SEP in judiciously consuming the extra energy of advanced (more powerful) nodes. SEP-FL yields longer stability period for higher values of extra energy. 2. Related work The energy model for the wireless sensor network with heterogeneous nodes and his setting is described as follows: Assuming the case where a percentage of the population of sensor nodes is equipped with more energy resources than the rest of the nodes. Taking m as the fraction of the total number of nodes n, which are equipped with α times more energy than the others. These powerful nodes are named advanced nodes, and the rest (1-m) x n is named normal nodes. We assume that all nodes are distributed uniformly over the sensor field. We consider architecture of a sensor network that is hierarchically clus- tered. Moreover, cluster heads are elected in each round and as a result the load is well distributed and balanced among the nodes of the network. The cluster head has to report to the sink and may expend a large quantity of energy, but this happens periodically for each node [2]. The energy model that we use in this study is illustrated in Figure 1:
- 2. IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014 113 Fig. 1 Radio energy consumption model In order to achieve an acceptable Signal-to-Noise Ratio (SNR) in transmitting L bits messages over a distance d, the energy expended by the radio is given by equation (1): (1) Where Eelec is the energy dissipated per bit to run the transmitter or the receiver circuit [2]. The optimal construction of clusters is very important because it is equivalent to the setting of the optimal probability for a node to become a cluster head. To evaluate the performance of clustering protocols, we define some metrics: A. Stability Period: is the time interval from the start of network operation until the death of the first sensor node. We also refer to this period as “stable region.” B. Instability Period: is the time interval from the death of the first node until the death of the last sensor node. We also refer to this period as “unstable region.” The protocol SEP improves the stable region of the clustering hierarchy process using the characteristic parameters of heterogeneity, namely the fraction of advanced nodes (m) and the additional energy factor between advanced and normal nodes (α). To increase the stable region, SEP attempts to maintain the constraint of well balanced energy consumption. Advanced nodes have to become cluster heads more often than the normal nodes. The new heterogeneous setting (with advanced and normal nodes) has no effect on the spatial density of the network [2]. On the other hand, the total energy of the system changes. Suppose that E0 is the initial energy of each normal sensor. The energy of each advanced node is then E0 .(1+ α). The total (initial) energy of the new heterogeneous setting is equal to: So, the total energy of the system is increased by a factor of 1+ α.m 3. Proposed approach This section describe the new method that we propose to improve the stable and unstable region of SEP by calculating the chance of each node to become cluster-head, unlike to SEP protocol that takes this parameter to elect CH as a random value, which causes the disadvantage of poor balancing energy in the network. To solve this problem, our approach (The SEP-FL Fuzzy logic approach to improve Stable Election Protocol for clustered heterogeneous WSN) is based on two deterministic criteria: the distance from the base station and the residual energy level of each node type. Our fuzzy system is divided in two Fuzzy Inference Systems (FIS), one for the advanced nodes and the other for the normal nodes. Each system consists of four steps denoted: fuzzifier, inference machine, rule base and defuzzifier, Figure 2 and 3 [5], show the different architectures of this system: EnergyNrm (3) DistToBS (3) Chance (9) SEP-FL-Nrm (mamdani) 9 rules Fig. 2 SEP-FL architecture for normal node EnergyAdv (3) DistToBS (3) Chance (9) SEP-FL-Adv (mamdani) 9 rules Fig. 3 SEP-FL architecture for advanced node 4. Our FIS Parameters and Rules In our proposed model, we use two parameters: energy level and distance to the base station of each non-CHs node. To study how much they are effecting the lifetime of the network, and to make these parameters more flexible, we divided each linguistic variable that we used to represent these parameters into three levels: low, medium, ( ) ( ) ( )0 0 0. 1 . . . . 1 . . 1 .n m E n m E n E mα α− + + = + (2)
- 3. IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014114 and high for energy level and Close, medium, and far for the distance to the BS. Moreover, many types of membership functions are available in the MATLAB Fuzzy Logic toolbox [5]. However, we used the Triangle and Trapezoidal membership functions because their degree is more easily determined [6]. Therefore, we chose to use them to present our parameters as illustrated in Figures 4, 5, and 6. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0 0.2 0.4 0.6 0.8 1 EnergyNrm Degreeofmembership Low Medium High Fig. 4 Fuzzy set of Energy level 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 DistToBS Degreeofmembership Close Medium Far Fig. 5 Fuzzy set of distance to BS 0 10 20 30 40 50 60 70 80 90 100 0 0.2 0.4 0.6 0.8 1 Chance Degreeofmembership V-w weak L-weak L-m Medium H-m L-S Strong V-S Fig. 6 Fuzzy set of chance value To determine the maximum values for our parameters in our FIS model, we have used equations (3), and (4): max energy initialenergy= (3) ( ) ( ) 22 max tan x ydis ceToBS BS BS= + (4) Where, ( ),x yBS BS is the position of the Base Station on x and y axis respectively. Since we have two parameters, each divided into three levels, we have 32 =9 possible chance values shows in Table 1 below that represents our fuzzy IF-THEN rules. Table 1 : Fuzzy Inference System IF-THEN rules Energy level Distance to the BS Chance to become CH Low Far Very weak Low Medium Weak Low Close Litter weak Medium Far Litter medium Medium Medium Medium Medium Close High medium High Far Litter strong High Medium Strong High Close Very strong 5. Determination of Cluster-Head Chance Value To obtain the Chance value, we aggregate the results of each rule. This process is called defuzzification. One of the most popular defuzzification methods is the centroid, which returns the centre of the area under the fuzzy set obtained aggregating conclusions. We use formula (5) to get the value of Chance for i node in r round: ( ) ( ) ( ) 1 1 , n j j j n j j u x x Chance i r u x = = = ∑ ∑ (5) Where u(xj) , is a membership function degree of set j, and xj is the output chance value on x-axis that intersect with u(xj). 6. Our algorithm protocol In every round, sensor node (advanced and normal) calculates the chance to become the cluster head using IF- THEN rules which are described in precedent section. After, it selects the maximum of these chances. If the maximum is less than the threshold T(s) (for advanced and for normal nodes) then the node becomes a cluster head and advertises this fact to other nodes around the cluster. The nodes that receive this message calculate the distance between the cluster head and itself and send a join–
- 4. IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014 115 message to the closest one of the cluster head to form a cluster. Equations (6) and (7) define the T(s) of different type of nodes, where Padv, Pnrm, are the probabilities to become cluster head for advanced and normal nodes respectively and r is current round. The G’ and G’’ are the sets of advanced and normal nodes that not elected as cluster heads in last 1/Padv and 1/Pnrm rounds per epoch respectively [2]. ( ) ' 1 1 mod 0 adv adv advadv adv P if S G P rT S P otherwise ∈ −= (6) ( ) '' 1 1 mod 0 nrm nrm nrmnrm nrm P if S G P rT S P otherwise ∈ −= (7) 7. Simulation and evaluation In this section, we evaluate the performance of our approach in MATLAB [5] in two different scenarios: α=1 and α=3 with 100 nodes are randomly distributed in a 100×100 m2 network. The initial energy of the sensors is E0=0.5 J. The simulation was performed for 10000 rounds. We use a simplified model showed in figure 1 for the radio hardware energy dissipation. We compare our approach to LEACH [3], LEACH-FL [4] and SEP [2] in Lifetime and Network’s conception energy. A. Network’s lifetime Although various definitions have been proposed in the literature, in this paper lifetime is considered as the time when the first node dies. Figure 7 and 9 illustrates the number of alive nodes with respect to the operation of the network in 10000 rounds for different scenarios. It is easy to find out that the proposed approach prolongs the lifetime of the stability period for normal and advanced sensors compared with other algorithms. Our proposed algorithm improves the overall network lifetime about 67.36% and 54 % compared to SEP algorithm results for α=1 and α=3 respectively. B. Consumption energy in the network Consumption energy in the network in each round can be a good metric to measure the energy efficiency of the algorithm. Figure 9 and 10 show the comparison of energy consumption rate of the four algorithms. In the proposed approach the consumption energy of network is less than others. This can be interpreted by the fact that our approach offers a considerable lifetime compared to other algorithms. On the other hand, we note that when the value of α increases, the lifetime of the network increases also. We can say that the doping network energy leads to an increase in its operating time. 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 80 90 100 Rounds AliveSensors LEACH LEACH-FL SEP SEP-FL Fig. 7 Network Lifetime (scenarios 1 with α=3) 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 80 90 Rounds ConsumptionEnergyintheNetworkAlive LEACH-FL SEP SEP-FL LEACH Fig. 8 Consumption energy of network (scenarios 1 with α=3) 0 500 1000 1500 2000 2500 3000 3500 4000 0 10 20 30 40 50 60 70 80 90 100 Rounds Alivesensors LEACH LEACH-FL SEP SEP-FL Fig. 9 Network Lifetime (scenarios 2 with α=1)
- 5. IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.1, January 2014116 Fig. 10 Consumption energy of network (scenarios 2 with α=1) 8. Conclusion Optimizing the energy consumption of heterogeneous wireless sensor network can be realized by using the fuzzy logic. Indeed, our approach shows, by simulation, the improvement of the stability period of the network before the death of the first node since it has two intervals of stability on the first normal nodes, the second related to advanced nodes that increases the lifetime and reduces the consumption of the energy stored in each node. SEP-FL is more energy efficient in prolonging the network life time using two parameters election fuzzy logic of heterogeneity in networks to evaluate the chance of sensors to become cluster head. As perspective of this work, we propose to simulate the protocol SEP-FL with a robust software simulation wireless sensor network and the realization of a prototype of a node joining our protocol. References [1] F. Akyildiz, W. Su, Y. Sankarasubramaniam and al., “A survey on sensor networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102-114, 2002. [2] G. SMARAGDAKIS, I. MATTA , A. BESTAVROS and al., “ SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks ” , Computer Science Department, Boston University. [3] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” In Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), January 2000. [4] G. Ran and al., “Improving on LEACH Protocol of Wireless Sensor Networks Using Fuzzy Logic”, Journal of Information & Computational Science 7: 3 767–775 (2010) [5] http://www.mathworks.com/ Fuzzy Logic Toolbox user’s guide 2012 [6] N. A. Torghabeh and al., “Cluster Head Selection using a Two-Level Fuzzy Logic in Wireless Sensor Networks”, 2nd International Conference on Computer Engineering and Technology IEEE Vol. 2 August 07 (2010) Baghouri Mostafa is an PhD student in the Laboratory of Systems Modeling and Analysis, Team: Communication Systems, Faculty of sciences, University of Abdelmalek Essaâdi, Tetouan Morocco, his research area is: Optimization of energy in the wireless sensors networks. He obtained a Master's degree in Electrical and Computer Engineering from the Faculty of Science and Technology of Tangier in Morocco in 2002. He graduated enabling teaching computer science for secondary qualifying school in 2004. In 2006, he graduated from DESA in Automatics and information processing at the same faculty. He work teacher of computer science in the high school. Chakkor Saad is an PhD student in the Laboratory of Systems Modeling and Analysis, Team: Communication Systems, Faculty of sciences, University of Abdelmalek Essaâdi, Tetouan Morocco, his research area is: intelligent sensors and its applications. He obtained a Master's degree in Electrical and Computer Engineering from the Faculty of Science and Technology of Tangier in Morocco in 2002. He graduated enabling teaching computer science for secondary qualifying school in 2003. In 2006, he graduated from DESA in Automatics and information processing at the same faculty. He work teacher of computer science in the high school.