Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 5, No. 5, 2014
84 | P a g e
www.ijacsa.thesai.org
Ameliorate Threshold Distributed Energy Efficient
Clustering Algorithm for Heterogeneous Wireless
Sensor Networks
MOSTAFA BAGHOURI
Department of Physics
Faculty of Sciences
University of Abdelmalek Essaâdi
Tetouan, Morocco
SAAD CHAKKOR
Faculty of Sciences
Tetouan, Morocco
ABDERRAHMANE HAJRAOUI
Faculty of Sciences
Tetouan, Morocco
Abstract—Ameliorating the lifetime in heterogeneous wireless
sensor network is an important task because the sensor nodes are
limited in the resource energy. The best way to improve a WSN
lifetime is the clustering based algorithms in which each cluster is
managed by a leader called Cluster Head. Each other node must
communicate with this CH to send the data sensing. The nearest
base station nodes must also send their data to their leaders, this
causes a loss of energy. In this paper, we propose a new approach
to ameliorate a threshold distributed energy efficient clustering
protocol for heterogeneous wireless sensor networks by excluding
closest nodes to the base station in the clustering process. We
show by simulation in MATLAB that the proposed approach
increases obviously the number of the received packet messages
and prolongs the lifetime of the network compared to TDEEC
protocol.
Keywords—Heterogeneous wireless sensor networks;
Clustering based algorithm; Energy-efficiency; TDEEC Protocol;
Network lifetime
I. INTRODUCTION
Wireless sensor network is the set of sensor nodes,
deployed in the hostile environment, in the goal to sense the
events detection, such temperature, pressure or vibration and
send their measurements toward a processing center called
sink [1], [2]. These tiny nodes are limited in their battery
capacity which its replacement is impossible. Furthermore, an
important part of energy is consumed in the communication
circuit which must be minimized. Because of those limitations,
the major wireless sensor networks’ challenging issues is the
energy consumption.
A number of research techniques about energy-efficient
have been proposed to solve these problems. In order to
support data aggregation through efficient network
organization, nodes can be partitioned into a number of small
groups called clusters. Each cluster has a cluster head, and a
number of member nodes [3]. Among WSN heterogeneous
protocols there is DEEC (Design of a distributed energy-
efficient clustering algorithm) [4]. This protocol is based on
the election of cluster head by the balance of the remaining
energy probabilities for each node. It uses the average energy
of the network as the energy reference. The cluster-heads are
elected by a probability based on the ratio between the residual
energy of each node and the average energy of the network.
DEEC has improved by a Stochastic approach SDEEC [5],
which reduces the intra-cluster transmission. In this protocol
the non-CH are going in to sleep mode to conserve more
energy. Another version of improved DEEC is DDEEC which
define a new residual energy threshold to elect CH [6]. On the
other hand TDEEC enhance the network lifetime by
introducing a new threshold based on the residual energy to
become CH [7]. The last version of TDEEC is ETDEEC
which prolong the lifetime by modifying the probabilities of
CH election based on the distance average between the CHs
and BS [8].
Otherwise, in order to improve the lifetime of the network,
ATDEEC employs a new technique which excludes closest
nodes to the base station from the clustering process. The
remainder of the paper is organized as follows. In section II
the main related works are summarized. Section III and IV
introduced the problem formulation and proposed approach.
Sections V and VI explains the network and the energy
models. Therefore, theoretical analysis are presented and
discussed in Section VII, whereas section VIII describes
performance analysis of the proposed method. Finally, Section
IX concludes our work, and discusses some future directions.
II. RELATED WORK
Currently, clustered routing protocols have gained actually
increasing attention from researchers because it’s potential in
extending WSN lifetime. Heinzelman et al. designed and
implemented the first distributed and clustered routing
protocol with low energy consumption LEACH [9]. Moreover,
the heterogeneous protocols are more energy efficient than the
homogeneous ones. Q. Li et al. have proposed Distributed
Energy Efficient Clustering Protocol (DEEC) [4]. This
protocol is based on multi-level and two level energy
heterogeneous schemes. The cluster heads are selected using
the probability utilizing the ratio between residual energy of
each node and the average energy of the network. The epochs
of being cluster-heads for nodes are different according to
their initial and residual energy. A particular algorithm is used
to estimate the network lifetime. Afterward, the network can
avoid the need of assistance by routing protocol [4]. TDEEC
[7] uses the same process of CH selection and estimation of

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average energy as in DEEC. At start of each round, the nodes
decide whether or not to become a CH by selecting a random
number within 0 and 1. If this selected number is lower than a
threshold, then the node becomes a CH for this round.
Simulation results show that in terms of network lifetime, both
EDEEC and TDEEC protocols are better than DEEC. TDEEC
provide best results compared to the three versions over
DEEC. Otherwise, Suniti Dutt et al [6], has proposed
ETDEEC protocol to enhance the network lifetime by
introducing a distance factor in CH probability. However, this
approach present a limitation lies in the fact that the network
instability observed after the death of the first node is caused
mainly by the bad energy distribution. It means that all nodes
not die approximately at the same time.
III. PROBLEM FORMULATION
In this paragraph we formulate the problem that we’ll
solve in the next sections. We consider a network with
nodes, which are uniformly distributed in a network
field as shown in Figure1.
Each node has a mission to send every time the data to the
base station which is located at the center of network. This
network divide in the cluster regions, and the cluster-heads
receive the data from the member nodes to transmitting toward
the base station. According to this model, it was found that the
member nodes that are closer to the base station must go
through a long path to route a data.
Contrariwise, they have the possibility to send the packet
messages directly to the base station (Figure 1). In this case,
these nodes should not go through the CH election process.
Consequently we can conserve the lost energy during this step
and we can prolong the network lifetime. To simulate this
problem, we present in the next section the model of the
studied network.
Fig. 1 Through the clustering process, all nodes must form clusters even those
who are closest to the base station
IV. PROPOSED METHOD
This paper proposes a new approach called Ameliorate
Threshold Distributed Energy Efficient Clustering (ATDEEC)
algorithm whose main objective is to increase the lifetime of
the network and to enhance the ability to deliver more packet
messages in the heterogeneous WSN by minimizing the
number of the nodes elected to become CH.
V. ENERGY MODEL
This study assumes a simple model for the radio hardware
where the transmitter dissipates energy for running the radio
electronics to transmit and amplify the signals, and the
receiver runs the radio electronics for reception of signals [7].
Multipath fading model ( power loss) for large distance
transmissions and the free space model ( power loss) for
proximal transmissions are considered. Thus to transmit
an message over a distance , the radio expends:
(1)
(2)
(3)
Where do is the distance threshold for swapping
amplification models, which can be calculated as
To receive an message the receiver expends:
(4)
To aggregate data signals of length , the energy
consumption was calculated as:
(5)
VI. NETWORK MODEL
This section describes the network model and other basic
assumptions:
1) N sensors are uniformly distributed within a square
field of area . The Base Station is positioned at the
center of the square region. The number of sensor nodes N to
be deployed depends specifically on the application.
2) All nodes are deployed randomly and can fall in the one
of two types of regions which can be defined by the threshold
distance from the base station.
3) In this case we define two types of nodes, Excluded and
not Excluded nodes. The Excluded are the nodes that not enter
in the clustering process because there are closed to the base
station and the other are far.
4) All sensors are heterogeneous, i.e., they not have the
same capacities.
5) All the sensor nodes have a particular identifier (ID)
allocated to them. Each cluster head coordinates the MAC and
routing of packets within their clusters. (see Fig. 2)

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Fig. 2 Wireless Sensor Network model
VII. THEORETICAL ANALYSIS
Let be the Expected distance of Exclude node
from the base station. Assuming that the nodes are uniformly
distributed, so it is calculated as follows:
(6)
(7)
If the density of sensor nodes is uniform throughout the
area then becomes independent of and . It is equal to
then:
(8)
According to the energy model proposed in section 5, the
energy consumed by each Excluded nodes is:
(9)
By combining the equations (8) and (9) the energy
consumed by each Excluded nodes is:
(10)
The energy consumed by the Not Excluded nodes is:
(11)
Where and are the energy consumed by each
cluster head and member node respectively and can be
calculated by:
(12)
(13)
Where is the average distance of not
Excluded node from the base station and is the average
distance between cluster members to CH.
Now and can be calculated as:
(14)
(15)
Where denoting the number of the clusters. The energy
total dissipated in a network is:
(16)
Where s is the number of the excluded nodes.
Using the Eq.11 to Eq. 16 the expected value of the energy
dissipated in the network is calculated as follows:
(17)
The optimal number of clusters can be found by letting
(18)
Where is the distance threshold for swapping
amplification models and R must be less the threshold Ro,
where .
The different forms of the calculation will lead to
different optimal settings depending on the values of,
and . The optimal probability for becoming a cluster-head
can also be computed as
In Figure 3, we show the average energy consumption by
each sensor node against varying numbers of clusters for
different values of number of excluded nodes s and threshold
distance R from base station.
While the number of cluster increases, the total energy
starts to decrease and reaches a minimum for clusters number
comprised between 10 and 18 depending on the value of s and
R. However, it is clearly shown that when s increases, the
energy consumption decreases and turns between 4.069 J and
1.473 J. These results are coincided with our conception and
our goals. In the next section we have evaluate these results by
computer simulation the network in Matlab.
0 10 20 30 40 50 60 70 80 90 100
0
10
20
30
40
50
60
70
80
90
100
Sink
R
Excluded nodes
Not excluded nodes

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0
5
10
15
20
25
1200
1300
1400
1500
1600
1700
1800
1900
5 10 15 20 25 30 35 40 45 50
Numberofcluster-head
FND(rounds)
Threshold distance R (m)
FND
Copt
Fig. 3 Variation of energy consumption for different values of R and s
depending on clusters number c.
VIII. SIMULATION RESULTS
In this section, we simulate the performance of ATDEEC
protocol under different scenarios using MATLAB. We
consider a model illustrate in the figure 2 with nodes
randomly distributed in a field. To compare
the performance of ATDEEC with TDEEC protocol, we
ignore the effect caused by signal collision and interference in
the wireless channel. The radio parameters used in our
simulations are shown in Table1.
TABLE I. ENERGY MODEL PARAMETERS
Parameter Value
Initial Node Energy 0.5J
N 100
50
5
10
0.0013
87 m
4000 Bytes
Rounds 8000
We define two performance metrics to evaluate our
protocol as: First Node Dies (FND), or stability period and
Last Node Dies (LND), or instability period.
First, we present an empirical result for the optimal
number of cluster-head Copt and optimal threshold distance to
the base station for our ATDEEC protocol shown in Figure 4.
The number of cluster-heads decreases from 10 to 45 meters.
This figure reveals that although the cluster-heads decreases
from 5 to 17, the FND improves significantly and has a
maximum value at 20 meters. Beyond this value, the curve
starts descending. The optimality of Copt lies around 17
cluster-heads for our setup. This result can be interpreted by
when the threshold distance R start to increase, the closer
nodes to the base station consume less energy, because they
send data directly to it. However, when this distance increases
the nodes become farther away and consume more energy.
Fig. 4 FND and Copt vs Threshold distance R
On other hand, we study three other performance metrics
such as, the number of live nodes per round, energy residual
and number of message packets for both ATDEEC and
TDEEC protocols. The simulation results are discussed below.
Fig. 5 Life time ATDEEC and TDEEC comparison
Figure 5 shows the network lifetime of ATDEEC and
TDEEC for threshold distance equal to 20m. Since the
TDEEC protocol is designed to be robust with respect to a
heterogeneous network, we test the performance of ATDEEC
against these criteria. Based on our experimental results, we
conclude that ATDEEC has a superior stability period life
time performance compared with TDEEC by an increase with
25% as shown in this same figure.
In the Figure 6, we emphasis our discussion on how each
node consumes its own residual energy in the network. This
energy is calculated during the network operation, by
observing the variation of energy levels between the nodes at
each round. The total initial energy of the network is 90 J
which decreases linearly up to 3000 rounds and after that there
is a difference from the round where first node dies in respect
to them. Energy residual per round for ATDEEC is more as
compared to TDEEC.
0 5 10 15 20 25 30 35 40 45 50
1.5
2
2.5
3
3.5
4
4.5
X: 10
Y: 1.473
Number of cluster
Energyconsumption(J)
X: 18
Y: 4.069
s=3 R=10
s=7 R=15
s=11 R=20
s=21 R=25
s=35 R=30
s=45 R=35
s=52 R=40
0 1000 2000 3000 4000 5000 6000 7000 8000
0
10
20
30
40
50
60
70
80
90
100
Rounds
Numberoflivenodes
ATDEEC
TDEEC

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25%
46%
184%
0%
40%
80%
120%
160%
200%
FND LND Throughput
Increasepercentage(%)
Performance metrics
Fig. 6 Total residual energy over rounds TDEEC and TDEEC
Referred to figure 7, it show clearly that proposed
approach provide a better throughput compared to TDEEC
protocol, this increase is justified by the life time enhancement
which give the improved ATDEEC protocol.
Fig. 7 Performance of the protocols
Generally, we can illustrate the increase of the proposed
protocol in the figure 8. It’s noted that the throughput
increases twice as much than TDEEC due to its energy
efficiency. Whereas, ATDEEC outperforms the FND of
TDEEC by 25% and by 46% for LND.
IX. CONCLUSION AND FUTURE WORK
In this paper, an energy efficient protocol ATDEEC has
been proposed to solve the problem of the closest nodes to the
base station which were consumed more energy in data
traffics.
Fig. 8 Performance metrics of the ATDEEC protocol
The simulation result by Matlab, demonstrate the ability of
developed algorithm to prolong the network lifetime
significantly and increase the number of packet messages
received by the base station. In the future work we’ll evaluate
this approach by the real-time performances and simulate it by
adequate simulator software.
REFERENCES
[1] Kay Romer and Friedemann Mattern. “The Design Space of Wireless
Sensor Networks”. IEEE Wireless Communications, 11(6):54–61,
December 2004.
[2] Kay Romer and Friedemann Mattern. “The Design Space of Wireless
Sensor Networks”. IEEE Wireless Communications, 11(6):54–61,
December 2004.
[3] V. Raghunathan, C. Schurgers, Park. S, and M. B. Srivastava, “Energy
aware wireless micro-sensor networks”. IEEE Signal Processing vol. 19
no. 2, pp. 40 –50, 2002,
[4] Li Qing, Qingxin Zhu, Mingwen Wang, “DEEC: Design of a distributed
energy-efficient clustering algorithm for heterogeneous wireless sensor
networks”, Computer Communications 29 (2006) 2230–2237.
[5] Elbhiri Brahim,Saadane Rachid, Driss Aboutajdine, “Stochastic
Distributed Energy-Efficient Clustering (SDEEC) for heterogeneous
wireless sensor networks”, ICGST-CNIR Journal, vol. 9, no. 2, pp. 11-
17, Dec. 2009.
[6] Elbhiri Brahim,Saadane Rachid, Sanaa El fkihi, Driss Aboutajdine,
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heterogeneous wireless sensor networks”, IEEE Communications and
Mobile Network (ISVC), 5th International Symposium on, oct. 2010.
[7] Parul Saini, Ajay.K.Sharma, “Energy Efficient Scheme for Clustering
Protocol Prolonging the Lifetime of Heterogeneous Wireless Sensor
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[8] Suniti Dutt, O. S. Khanna, “An Enhanced Energy Efficient Clustering
Scheme for Prolonging the Lifetime of Heterogeneous Wireless Sensor
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8887) vol. 76, no.8, August 2013
[9] Wendi R. Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan,
“Energy efficient communication protocol for wireless microsensor
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2000.
0 1000 2000 3000 4000 5000 6000 7000 8000
0
10
20
30
40
50
60
70
80
90
100
Rounds
Residualenergyinthenetwork(J) ATDEEC
TDEEC
0 1000 2000 3000 4000 5000 6000 7000 8000
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
5
Rounds
Numberofpacketssenttothebasestation
ATDEEC
TDEEC

Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

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