 Open Access
 Total Downloads : 66
 Authors : Moulay Lahcen Hasnaoui , Mohamed Saad Azizi
 Paper ID : IJERTV6IS100148
 Volume & Issue : Volume 06, Issue 10 (October 2017)
 Published (First Online): 27102017
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Improving Lifetime in Heterogeneous Wireless Sensor Networks
Moulay Lahcen Hasnaoui, Mohamed Saad Azizi Research Team: ISIC ESTM, LMMI Laboratory ENSAM Moulay Ismail University
Meknes, Morocco
1Mostafa Saadi, 2Abderrahim Beni Hssane
1ENSA Khouribga, Hassan 1st University
2Faculty of Sciences El Jadida, Chouaib Doukkali University, Morocco.
AbstractWireless Sensor Networks (WSNs) consist of a large number of energylimited sensor nodes that are densely deployed in a large geographical region. For WSNs, energy efficiency is always a key design issue to improve the life span of the network. The clustering Algorithm is a kind of key technique used to reduce energy consumption and to increase scalability and lifetime of the network. In this paper, we propose and evaluate a distributed energyefficient clustering algorithm for heterogeneous WSNs, which is called Improved LEACHE (ILE). This protocol is a LEACHE improvement. In ILE, the clusterheads are elected by a probability based on the ratio between residual energy of each node and the remaining energy of the network. Also, it uses a second hierarchical level by selecting a cluster head for data transmission. Furthermore, we consider a multilevel heterogeneous network in order to study the impact of heterogeneity on wireless sensor Networks. In this case we propose an energyefficient multilevel clustering algorithm called ILEM. Finally, Simulation results show that the network lifetime and energy efficiency are much better in our proposed protocols ILE and ILEM than the existing protocols.
Keywords WSN; Clustering; Lifetime; Energy Effeciency; Heterogeneity.

INTRODUCTION
Recent advances in wireless communications and electronics have enabled the development of lowcost, lowpower, multifunctional sensor nodes that are small in size and communicate in short distances. A wireless sensor network (WSN) is composed of a large number of sensor nodes that are deployed in ad hoc manner in an unreachable field to give the enduser, the ability to instrument, observe, and react to events and phenomena in a specified environment. Wireless Sensor Networks provide unforeseen applications: from military applications such as battlefield mapping and target surveillance, to creating contextaware homes; the number of applications is endless [1]. In most of the applications, sensors are required to detect events and then communicate the collected information to a distant base station (BS) where parameters characterizing these events are estimated. Since the cost of transmitting information is higher than computation.
Clustering sensors into groups, so that they communicate information only to cluster heads and then the cluster heads communicate the aggregated information to the processing center, saves energy [2], [3] and [4]. Thus, it is advantageous to organize the sensors into clusters; where the data gathered and fused by the sensors is communicated to the BS through a hierarchy of clusterheads. The clusterheads, which are elected periodically by certain clustering algorithms, aggregate the data of their cluster members and send it to the base station, from where the endusers can access the sensed data. Thus, only some nodes are required to transmit data over a long distance and the
rest of the nodes will need to complete short distance transmission only. Therefore, more energy is saved and the overall network lifetime can be extended.
There are two kinds of clustering schemes. The clustering algorithms applied in homogeneous networks are called homogeneous clustering schemes, where all nodes have the same initial energy, such as LEACH [3], PEGASIS [5], and HEED [6], and the clustering algorithms applied in heterogeneous networks are referred to as heterogeneous clustering schemes [7], where all the nodes of the sensor network are equipped with different amount of energy, such as SEP [8], MLEACH [9], EECS [10], LEACHB [11]. WSNs are more
possibly heterogeneous networks than homogeneous ones. Thus, the protocols should be fit for the characteristic of heterogeneous wireless sensor networks. Moreover, in [12, 13], they propose LEACHE protocol, which uses a new conception based on the energy left in the network.
Based on LEACHE protocol, we develop and validate a newest Improved LEACHE algorithm called ILE. This protocol is proposed to increase the whole network lifetime on a heterogeneous network with a BS located far away from the sensing area. ILE introduces the second level hierarchical concept based on maximum energy, which improves and optimizes the use of the energy dissipated in the network like TLLEACH [14]. The use of two levels of clusters for transmitting data to the BS, leverages the advantages of small transmit distances and reduces the number of transmission data to the BS. As a consequence, fewer cluster heads are required to transmit far distances to the BS. This permits a better distribution of the energy load through the sensors in the network and increases the whole network lifetime.
The remainder of this paper is organized as follows. Section 2 presents the related work. Section 3 exhibits the details and analyzes the properties of ILE. Section 4 evaluates the performance of ILE by simulations and compares it with LEACH and LEACHE. Finally, Section 5 gives concluding remarks.

RELATED WORKS
The main goal of clusterbased routing protocol is to efficiently maintain the energy consumption of sensor nodes by involving them in multihop communication within a cluster and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the sink and transmission distance of sensor nodes [1618]. In this section, we make a few statements and assumptions about the network scheme and introduce the radio model used in this work.

Heterogeneous Network Model
In this study, we describe the network model. Assume that there are N sensor nodes, which are uniformly dispersed within a M x M square region (Fig. 1). The nodes always have data to transmit to a base station, which is often far from the sensing area. This kind of sensor network can be used to track the military object or monitor remote environment. The network is organized into a clustering hierarchy, and the clusterheads execute fusion function to reduce correlated data produced by the sensor nodes within the clusters. The clusterheads (Fig. 2) transmit the aggregated data to the base station directly. We assume that the nodes are stationary as supposed in [12].
In the twolevel heterogeneous networks, there are two types of sensor nodes, i.e., the advanced nodes and normal nodes. Note
0 the initial energy of the normal nodes, and the fraction of the advanced nodes, which own a times more energy than the normal ones. Thus there are advanced nodes equipped with
100
90
80
70
60
50
40
30
20
10
0
0 10 20 30 40 50 60 70 80 90 100
initial energy of 0(1 + ), and (1 ) normal nodes equipped with initial energy of0. The total initial energy of the twolevel heterogeneous networks is given by:
= (1 )0 + 0(1 + ) = 0(1 + )
100
90
80
Fig.2. Dynamic cluster structure by ILE algorithm: o (simple node), * (cluster head).
Furthermore, we use in this study a similar energy model as proposed in [12]. According to the radio energy dissipation model illustrated in (Fig. 3), and in order to achieve an acceptable SignaltoNoise Ratio (SNR) in transmitting an Lbit message over a distane , the energy expended by the radio is given by :
+ 2 , < 0
(, ) = {
70
+
4, 0
60
50
40
30
20
10
0
0 10 20 30 40 50 60 70 80 90 100
Fig.1. 100 nodes randomly deployed in the network
Where is the energy dissipated per bit to run the transmitter or the receiver circuit, and 2and 4 depend on the transmitter amplifier model used and is the distance between the sender and the receiver. We have fixed the value of 0 at 70 meters.
Fig.3. Radio Energy Dissipation Model

Problem Speech
Since in most WSN applications the energy source is a battery, energy plays an important role in WSN [1921]. Therefore, preserving the consumed energy of each node is an important goal that must be considered when developing a routing protocol for WSNs. To increase the whole network lifetime, we have developed energy efficient clustering algorithms called ILE. Based on a balanced way to elect networks clusters heads, ILE achieves a large reduction in the energy dissipation. In the next section, we describe the ILE algorithm in details.


IMPROVES LEACHE (ILE)
Our ILE uses the same clustering algorithm as LEACHE that is it uses the same strategy in Clusters Head selection, Clusters formation, and Schedule Creation (TDMA) but differs in Data transmission. ILE algorithm can be summarized as
Where () is the current energy of node i, is the desired number of cluster, and () is the remaining energy of the network per round :
=1
follow:
For each nodei
() =
()
(5)
If (nodei is NCH) then
Appropriate CH election Send data to CH
Else If(nodei is not MaxCH) then
Data aggregation(nodes)
If (dtoBS>dtoMaxCH) then
Send to MaxCH Else
Send to BS
End if
Else
first data aggregation (nodes) Second data aggregation (CHs) Send to BS
End if
End if
End for
To use the probabilities in (Eq. 4), each node must have an estimate of the remaining energy of all nodes in the network per round. To compute () by (Eq. 5), each node should have the knowledge of the total energy of all nodes in the network. We will give an estimate of () as follow:
() = (1 ) (6)
where R denotes the total rounds of the network lifetime. It means that every node consumes the same amount of energy in each round, which is also the target that energyefficient algorithms should try to achieve. The value of R is:
=
(7)
Where denotes the total energy dissipated in the network during a round r is given by:
= [2 + + ( 1)4 + 2 + 4 ]
NCH: not a CH
CH: Cluster head
MaxCH : the CH with the maximum report between residual energy and dtoBS
dtoBS: distance to the BS
dtoMaxCH : distance to MaxCH
(8)
Furthermore, ILE introduces two level hierarchical concept of clusters for transmitting data to the BS. Thus, it leverages the advantages of multihop transmission and reduces the disadvantages of singlehop transmission. In this way, fewer cluster heads are required to transmit far distances to the BS. We consider a network with N nodes, uniformly distributed within
Where k is the number of clusters, is the data aggregation cost expended in the clusterheads, dtoBS is the average distance between the clusterhead and the base station, dtoMaxCH is the distance between the clusterheads and the Maximum energy Cluster Head (MaxCH), and dtoCH is the average distance between the cluster members and the clusterhead. Assuming that the nodes are uniformly distributed, by using the result in [12, 15] we can get the equations as follow:
MÃ—M square region and that the network topology remains unchanged over time and the BS location is (x = 50, y = 175). In ILE, We get the probability threshold, which each node uses
=
2
(9)
to determine whether itself to become a clusterhead in each round, as follow:
= 1
2
( + )2 + ( + )2
2 (10)
( ) = { 1 ( 1 )
0
= 2 2
(11)
Where is the set of nodes that are eligible to be cluster heads at round. In each round, when node finds it is eligible to be a cluster head, it will choose a random number between 0 and 1.
=
2 2
(12)
If the number is less than the threshold(), the node becomes a cluster head during the current round. Also, is defined as follow:
() = { () , 1}
Substituting equations (12, 11, 10, 9, 8, 7, 6, 5, and 4) into equation (3), we obtain the probability threshold. Each node that has elected itself a clusterhead for the current round broadcasts
an advertisement message to the rest of the nodes. For this
()
(4)
clusterheadadvertisement phase, the clusterheads use a
CSMA MAC protocol, and all clusterheads transmit their advertisement using the same transmit energy. The noncluster head nodes must keep their receivers on during this phase of set up to hear the advertisements of all the clusterhead nodes. The sent messages content in addition the Id nodes, the information coordinates.
After this phase is complete, each nonclusterhead node decides the cluster to which it will belong for this round. This decision is based on the received signal strength of the advertisement. Assuming symmetric propagation channels, the clusterhead advertisement heard with the largest signal strength is the clusterhead to whom the minimum amount of transmitted energy is needed for communication. In the case of ties, a random clusterhead is chosen [3].
Based on the information coordinates included on the message broadcasted, the CHs elected can select the Maximum energy Cluster Heads MaxCH. Consequently, the CH with the important energy will be the MaxCH in this round. This last node collects all data coming from all CHs, compress it into a single signal and send it directly to the base station. We have chosen the MaxCH as intermediate hierarchical level, because the latter granted the transmission for long time. In fact, they have not waste energy in long transmission to the BS.
Each non cluster heads sends its data during their allocated transmission time (TDMA) to the respective cluster head. The CH node must keep its receiver on in order to receive all the data from the nodes in the cluster. When all the data is received, the cluster head node performs signal processing functions to compress the data into a single signal. When this phase is completed, each cluster head can send the aggregated data to the MaxCH. After that, each non cluster head can turn off to sleep
substitute pi into Eq. (3), and get the election threshold(), which is used to decide if node should be a clusterhead in the current round.

SIMULATION RESULTS
In this section, we evaluate the performance of ILE protocol using MATLAB. We consider a wireless sensor network with N
= 100 nodes randomly distributed in a 100m x 100m field. We assume the base station is far away from the sensing region. To compare the performance of ILE with other protocols, we ignore the effect caused by signal collision and interference in the wireless channel. The radio parameters used in our simulations are shown in TABLE I. We assume that all nodes know their location coordinates. The protocols compared with ILE include LEACH, and LEACHE. The Base station is located far away from the sensing area. It was placed at location (x=50, y=175). We will consider following scenarios and examine several perfomance measures.
After deployment of WSN, the nodes consume energy during the course of the WSN lifetime. In fact, energy is removed whenever a node transmits or receives data and whenever it performs data aggregation using the radio parameters shown in TABLE I. Once a node runs out of energy, it is considered dead and can no longer transmit or receive data.
First, we observe the performance of LEACH, LEACHE, and ILE under two kinds of twolevel heterogeneous networks. Fig. 4 shows the results of the case with m = 0.1 and a = 5, and Fig.5. shows the results of the case m = 0.2 and a = 3.
We define stable time as time until the first node dies, and unstable time the time from the fist node dies until the last node dies. In other words, lifetime is the addition of stable time and unstable time.
mode.
Parameter
Value
5 nJ/bit
10 pJ/bit/2
0.0013 pJ/bit/4
0
0.5 J
5 nJ/bit/message
0
70 m
Message size
4000 bits
0.1
Round
20 seconds
TABLE I. RADIO CHARACTERISTICS USED IN OUR SIMULATIONS
100
LEACH
LEACHE
90
80
Number of nodes alive
70
60
50
40
30
20
10
0
a=5 and m=0.1
ILE
Initially, all the nodes need to know the initial energy Einitial and lifetime R of the network, which can be determined a priori. In our ILE protocol, the base station could broadcast the initial energy and estimate value R of lifetime to all nodes. When a new epoch begins, each node will use this information to compute its probability by Eqs. (6) and (4). Node will
0 500 1000 1500 2000 2500 3000
Time(Round)
Fig.4. Performance of LEACH, LEACHE, and ILE under twolevel heterogeneous networks a=5, =0.1: Number of nodes alive over time.
It is obvious that the stable time of ILE is large compared to that of LEACH and LEACHE Fig.4. and Fig.5. The stable time metric is important to be longer in the sense that it gives the end user with reliable information of the sensing area. This reliability
is vital for sensitive application like tracking fire in forests. LEACHE performs better than LEACH, but we can see that the unstable time of LEACHE is also larger than our ILE protocol. It is because the advanced nodes die more slowly than normal nodes in LEACHE. This metric is important to be narrow in order to give clear idea about time of reenergizing the WSN to extend the network lifetime and to avoid unreliable information from sensing field.
LEACH LEACHE ILE
1800
1600
Round first node dies
1400
1200
1000
100
90
80
Number of nodes alive
70
60
50
40
30
20
10
0
a=3 and m=0.2
LEACHE ILE
LEACH
0 500 1000 1500 2000 2500 3000
Time(Round)
800
600
400
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
a
Fig.6. Round first node dies when a is varying.
We observe that LEACH fails to take full advantage of the extra energy provided by the heterogeneous nodes. The stability period of LEACH is very short and nodes die at a steady rate. This is because LEACH treats all the nodes without discrimination. The stability period of ILE is much longer than
Fig.5. Performance of LEACH, LEACHE, and ILE under twolevel
heterogeneous networks a=3, m =0.2: Number of nodes alive over time.
TABLE II
a=3, m =0.2
LEACH
LEACHE
ILE
FND
597
976
1238
HNA
932
1279
1322
TABLE III
a=5, m =0.1
LEACH
LEACHE
ILE
FND
615
858
1175
HNA
867
1132
1213
TABLE II and TABLE III show the comparison between all nodes in terms of FND (First Node Dies) and HNA (Half Node Alive).

FND: indicate the round when first node dies.

HNA: indicate the round when half node still alive.
The TABLE II and TABLE III show that ILE increases the lifetime of the whole network and performs better than LEACH and LEACHE in term of the first node dies.
Second, we run simulation for our proposed protocol ILE to compute the round of the first node dies when m and a are varying and compare the results to LEACH and LEACHE
protocols.
We increase the fraction of the advanced nodes from 0.5 to 5, Fig. 6 shows the number of round when the first node dies. We increase the fraction of the advanced nodes from 0.1 to 0.9, Fig.7. shows the number of round when the first node dies. We observe that LEACH takes few advantages from the increase of total energy caused by increasing of and a. The stability period of LEACH keeps almost the same in the proess.
that of LEACH and LEACHE. This is because ILE is an energy aware protocol, which elects clusterhead by taking initial energy and residual energy into account at the same time.
LEACH LEACHE ILE
2000
1800
1600
Round first node dies
1400
1200
1000
800
600
400
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Fig.7. Round first node dies when m is varying.
Third, we run simulation for our proposed protocol ILE to compute the number of received messages by the BS over time and compare the results to LEACH and LEACHE protocols. Fig.8. shows that the number of messages delivered by ILE to the BS are greater than the others ones; this means that ILE is a more efficient protocol.
4
x 10
Number of messages received at the BS
4.5
4
3.5
3
2.5
2
1.5
1
0.5
a=5 and m=0.1
LEACH
LEACHE ILE
1400
1200
Round first node dies
1000
800
600
400
200
0
LEACH
LEACHE ILE
0 20 40 60 80 100 120 140 160 180 200
Network diameter(m)
0
0 500 1000 1500 2000 2500 3000
Time(Round)
Fig.8. Performance of LEACH, LEACHE, and ILE under twolevel heterogeneous networks a =5, =0.1: Number of message received in base
station over time.
Fourth, we run simulation for our proposed protocol ILE to compute the number of received messages at the BS over energy dissipation and compare the results of LEACH and LEACHE protocols. Fig.9. shows that the number of messages delivered by ILE to the BS are greater than the others ones; this means that ILE is a more efficient of energy consumption protocol. In other words, it is an energyaware adaptive clustering protocol.
Fig.10. Performance of LEACH, LEACHE and ILE under twolevel heterogeneous networks a =5, m =0.1: Round first node dies over the size of
the experiment region (Network diameter).
Sixth, we run simulation for our proposed protocol ILE to compute the number of received messages at the BS over the size of the experiment region and compare the results to LEACH and LEACHE protocols.
Fig.11. shows that the number of messages delivered by ILE to the BS remain greater than the others ones; even if the experiment region changes.
According to the simulation results, we can obviously state that ILE is more efficient than LEACH and LEACHE.
4
18
x 10
3.5
Number of messages received at the BS
3
4
LEACH
LEACHE ILE
Number of messages received at the base station
LEACHE
LEACH ILE
x 10
16
14
12
2.5
10
2 8
6
1.5
4
1 2
0.5
0
0 20 40 60 80 100 120 140 160 180 200
Network diameter (m)
0
0 10 20 30 40 50 60 70 80
Energie spent(Joule)
Fig.9. Performance of LEACH, LEACHE and ILE under twolevel heterogeneous networks a =5, =0.1: Number of messages received at BS
over Energy dissipation (Joules).
Fifth, we run simulation for our proposed protocol ILE to compute the number of received messages at the BS over the size of the experiment region and compare the results to LEACH and LEACHE protocols. Fig.10. shows that the round first dies remain greater than the others ones, when the experiment region is greater than 25×25 m2.
Fig.11. Performance of LEACH, LEACHE and ILE under twolevel heterogeneous networks a =5, m =0.1: Number of messages received at BS
over the size of the experiment region (Network diameter).


ILEM : MULTILEVEL ILE
We consider the multilevel heterogeneous network in order to study the impact of heterogeneity on wireless sensor Networks. In this case we propose an energyefficient multi level clustering algorithm called ILEM. For multilevel heterogeneous networks, initial energy of sensor nodes is randomly distributed over the close set [E0, E0(1 + amax)], where E0 is the lower bound and amax determine the value of the maximal energy. Initially, the node si is equipped with initial energy of E0(1 + ai), which is ai times more energy than the lower bound E0.
The total initial energy of the multilevel heterogeneous networks is given by:
stability time than LEACH and LEACHE, which increases the efficiency of the network. We notice the same results for HNA.
Etotal
N
= E0
i=1
(1 + ai) = E0
N
(N + (ai))
i=1
A longer stable time metric is important because it gives the end user reliable information of the sensing area, which extend the network lifetime. This reliability is vital for sensitive applications such as tracking fire in forests.
As in twolevel heterogeneous networks, the clustering algorithm should consider the discrepancy of initial energy in multilevel heterogeneous networks. For multilevel heterogeneous networks, the initial energy of nodes are randomly distributed in [E0, 4E0]. To prevent the affection of random factors, the network is equipped with the same amount of initial energy.
In Fig.12. detail views of the behavior of LEACH, LEACH E, and ILEM are illustrated. We observe that LEACH fails to take full advantage of the extra energy provided by the heterogeneous nodes. The stability period of LEACH is very short and nodes die at a steady rate. This is because LEACH treats all the nodes without discrimination. We observe that the stable region of ILEM is also larger than LEACH and LEACH E.
Fig.12. Performance of LEACH, LEACHE, and ILEM under multilevel heterogeneous networks. Number of nodes alive over time.
TABLE IV
LEACH
LEACHE
ILEM
FND
547
965
1311
HNA
972
1367
1521
The TABLE IV shows that ILEM increases the lifetime of the whole network and performs better than LEACH and LEACHE in term of the first node dies.
Fig.14. Performance of LEACH, LEACHE, and ILEM under multilevel heterogeneous networks. Number of message received in base station over
time.
Moreover, Fig.12., Fig.13. and Fig.14. show that the performances due to our modifications are very important. Also, the messages delivered by ILEM are more than that of LEACH and LEACHE. This means that ILEM is more efficient than LEACHE.
Fig.15. gives the total network energy dissipation in every transmission round. The network remaining energy decreases
1600
1400
Time (Round)
1200
1000
547
800
600
400
200
0
1367
972
965
152
1
131
1
FND HNA
rapidly in the LEACH and LEACHE protocols.
LEACH LEACHE ILEM
Fig.13. Round for FND and round for HNA in the network.
Fig.13. shows the comparison between all nodes in terms of FND (First Node Dies) and HNA (Half Node Alive). Obviously, we can remark that our protocol ILEM have a larger period of
Fig.15. Performance of LEACH, LEACHE, and ILEM under multilevel heterogeneous networks. Energy dissipation over time.
Fig.16. shows the results of number of nodes alive over number of message received in base station. Its obvious that the number of messages delivered by ILEM to the BS are greater than the others ones, this means that ILEM is a more efficient protocol in terms of energy consumption. In other words, it is an energyaware adaptive clustering protocol.
Fig.16. Performance of LEACH, LEACHE, and ILEM under multilevel heterogeneous networks. Number of nodes alive over number of message
received in base station.

CONCLUSION
We describe ILE, an energyaware adaptive clustering protocol used in heterogeneous wireless sensor networks. In ILE, every sensor node independently elects itself as a cluster head based on its initial energy and residual energy. To control the energy expenditure of nodes by means of adaptive approach, ILE use the average energy of the network as the reference energy. Thus, ILE does not require any global knowledge of energy at every election round. Therefore, ILE uses the two level hierarchical concept which offers a better use and optimization of the energy dissipated in the network. We also study the impact of multilevel heterogeneous networks. Simulation results demonstrate that our proposed protocols ILE and ILEM are effective in prolonging the network lifetime.
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