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Lifetime Enhancement of WSN Based on Improved LEACH with
Cluster Head Alternative Gateway
Abdel-Nasser Ateeq
abdelnasser3teeq@gmail.com
Department of Networks & Information Security
An-Najah National University
Nablus, Palestine
Israa Obaid
Qaddomi.Obaid@gmail.com
Department of Networks & Information Security
An-Najah National University
Nablus, Palestine
*Othman Othman
othman.omm@najah.edu
Department of Networks & Information Security
An-Najah National University
Nablus, Palestine
*Ahmed Awad
ahmedawad@najah.edu
Department of Networks & Information Security
An-Najah National University
Nablus, Palestine
ABSTRACT
Wireless Sensor Network (WSN) is a self-congured network of
light-weight nodes, that are used in many applications by sending
their data through this network to a Base Station (BS), which in
turn, delivers the data to its nal destination. In WSN, the sensor
node’s energy is usually limited, and thus, the overall power con-
sumption of the network is a real challenge. Numerous methods
have been proposed to overcome this challenge by improving the
overall power consumption and thus, prolonging the lifetime of
the network. Low Energy Adaptive Clustering Hierarchy (LEACH)
protocol has been considered as a leading routing protocol in WSN,
due to its low power consumption resultant from its cluster-based
behavior. However, for dense networks, LEACH suers from large
burden over cluster head (CH), which might result in packet loss
due to the induced congestion in the CH. In this paper, an improved
algorithm based on LEACH is proposed to maximize the lifetime of
WSN, by using another node to relieve CH’s burden. The routing
path selection process is rened through selecting a new node that
has the highest residual energy in each cluster to be an alternative
gateway for the cluster head. Then, the routing path will be de-
cided based on distance. This modied algorithm outperforms the
original LEACH by 4.35% increase in the residual energy, which
prolongs the network lifetime. However, there is a slight increase
in the overhead ratio.
CCS CONCEPTS
•Computer systems organization →Embedded systems
;Re-
dundancy; Robotics; •Networks →Network reliability.
Corresponding Authors: ahmedawad@najah.edu, othman.omm@najah.edu.
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ICFNDS ’20, November 26–27, 2020, St.Petersburg, Russian Federation
©2020 Association for Computing Machinery.
ACM ISBN 978-1-4503-8886-3/20/11. . . $15.00
https://doi.org/10.1145/3440749.3442615
KEYWORDS
Wireless Sensor Network (WSN), Energy, Cluster, LEACH protocol,
Cluster Head (CH), Base Station (BS), Alternative Gateway (AG),
Residual Energy, Ad-hoc, Routing Protocol, Distance
ACM Reference Format:
Abdel-Nasser Ateeq, Israa Obaid, *Othman Othman, and *Ahmed Awad.
2020. Lifetime Enhancement of WSN Based on Improved LEACH with
Cluster Head Alternative Gateway. In The 4th International Conference on
Future Networks and Distributed Systems (ICFNDS) (ICFNDS ’20), November
26–27, 2020, St.Petersburg, Russian Federation. ACM, New York, NY, USA,
6 pages. https://doi.org/10.1145/3440749.3442615
1 INTRODUCTION
A Wireless Sensor Networks (WSN) consist of low power devices
that are geographically-distributed. They communicate with each
other using radio signals to send the data to a Base Station (BS).
A typical sensor node has several components: a radio transceiver
with an antenna that sends or receives signals, a micro-controller
that processes the data and schedules tasks, and batteries to provide
the energy supply [
3
]. The communication power is the principal
contributor for the entire energy consumption in the sensor node.
To minimize the overall energy consumption of the sensor network,
several protocols have been proposed in the literature [
9
][
8
] [
2
]
[15].
Two types of routing protocols are considered depending on
the architecture of the WSN. The rst type, adopts a at network
architecture in which all nodes send their data immediately to the
BS. The second, adopts the hierarchical network architecture; such
as clustering [3].
Clustering is one of common methods used to achieve energy
eciency, scalability, and prolonging the network lifetime. In the
clustering technique, a WSN is divided into clusters (groups of
nodes). In each cluster, there is a node that acts as a cluster head
(CH). The members in the cluster (Non-CH) sense and transmit
their data to the CH. After that, the CH sends the data to the BS[
12
].
LEACH (Low Energy Adaptive Clustering Hierarchy) protocol
is a cluster-based protocol, that has been developed to prolong the
lifetime of a WSN. LEACH protocol achieves this by preserving
energy in sensor nodes and minimizing the size of the routing table.
ICFNDS ’20, November 26–27, 2020, St.Petersburg, Russian Federation Abdel-Nasser et al.,
Nevertheless, it still has some shortcomings as mentioned below
[5]:
•
LEACH protocol assumes that all sensor nodes have enough
energy to communicate with the BS. So, more energy is
consumed if sensor nodes are far from the BS.
•
LEACH protocol has been designed to work well with small
deployment environments. By increasing the size of the net-
work, the CHs that are located far away from the BS consume
more of their energy rapidly.
•
Cluster Head (CH) is chosen in a random manner, overlook-
ing the residual energy in the nodes.
•
CH is chosen by having each sensor node to generate a ran-
dom number. If a node’s number is less than a predened
threshold, it will become the cluster head. The energy ef-
ciency of the sensor node is signicantly aected by the
number of nodes using that CH.
In this paper, we propose an enhanced version of LEACH to tackle
the above drawbacks. Our proposed version of LEACH selects the
best path for routing through electing an Alternative Gateway (AG)
node on the cluster level with taking into consideration both the
residual energy and the distance to the BS in the AG selection
process. Thus, the trac is steered to the closest gateway for each
node in the cluster, which eventually results in prolonging the
network lifetime. Our contributions are summarized as follows:
•
An enhanced version of LEACH protocol is proposed to
prolong the network lifetime of the WSN.
•
Alternative Gateway (AG) node is selected on the cluster
level to steer the trac in such a way that both the residual
energy and distance to the BS are included in the selection
recipe.
•
The proposed version of LEACH protocol is evaluated on sev-
eral benchmarks and its performance is evaluated in terms
of the total residual energy in the network alongside with
the additional overhead.
The rest of the paper is organizing as follows. Section 2 shows a
brief description of the related work performed on the LEACH pro-
tocol. Section 3 presents the assumptions. After that, the proposed
approach is discussed in Section 4. Simulation results and analysis
are discussed in Section 5. At last, the conclusions and future work
are introduced in Section 6.
2 RELATED WORKS
In general, LEACH is one of the most popular cluster-based routing
protocols. It introduces a random-based technique to elect CHs,
which in turn, aggregate and deliver the data sent by cluster mem-
bers to the BS. Due to the limited power budget, many researches
have been introduced to attain lifetime maximization for sensor
nodes by reducing energy consumption.
In [
9
] the authors introduced an assistant cluster head approach.
Besides, this approach depends on the following metrics; the num-
ber of members in each cluster, the remaining energy of each node,
and the geographical location of nodes to be a cluster head. The at-
tended approach prolongs lifetime by reducing the energy-draining
of member nodes in a cluster. But, according to the number of
measures taken into account, this technique demands complex op-
erations that causes more delays [3].
In [
8
] the authors introduced a Time-based CH selection al-
gorithm to construct the cluster without the need for any global
information. However, the proposed algorithm suers from a big
gap energy consumption between CHs and their member nodes
[3].
In [
2
] the authors introduced a method for minimizing energy
consumption of each node by electing a sensor node to become a
cluster head based on the highest residual energy, and the number
of neighbors close to the base station. In [
14
] the authors proposed
LEACH-B (LEACH-Balanced). This protocol presents an approach
to elect CH due to its residual energy within two stages. The rst
selection of CH is performed according to the original LEACH. But,
starting from the second selection they alter the number of CHs
based on the node’s residual energy. The proposed protocol extends
the network lifetime by achieving the balancing of network energy
consumption. However, the proposed approaches in [
2
][
14
] do not
consider the existence of small-sized clusters. In addition, they fo-
cus only on energy consumption by threshold and nodes’ residual
energy [3].
In [
15
] the authors proposed HEED protocol. They considered
the energy as the main gauge for a sensor node to be elected as
a CH. Inter cluster communication is allowed thereby reducing
long distance transmission, which saves energy to a large extent.
However, in this protocol, several iterations are performed to form
clusters, which results in signicant overhead in the network [4].
In [
11
] the authors have introduced an improved LEACH called
MG-LEACH, which adds a new phase called set building phase,
which takes place before the setup phase and steady-state phase
in the original LEACH. The new phase constructs sub-groups by
communicating with BS, due to the network density, threshold, and
residual energy. However, this approach adds an additional burden,
because of the need for communicating with BS every round. Also,
the burden over the CH has not been distributed.
In [
1
] the authors introduced a modied algorithm of E-LEACH
called ME-LEACH. The introduced algorithm has tried to lengthen
the network lifetime by enhancing the routing path. The signicant
rule of this method is to nd the optimal CH; that is, each CH will
choose the best CH due to its distance and residual energy to act as
its next hop. This method has succeeded in prolonging the network
lifetime, but it ignored the non-CH nodes when choosing the next
hop phase.
In [
10
] the authors suggested an algorithm to use two static BSs
to prolong the network lifetime. Also, they proposed a method to
calculate the optimum position of the static BSs. Although, the
proposed approach succeeded in lifetime prolonging, it considers
only the distance metric and ignores the residual energy metric.
All of the mentioned algorithms and methods have somehow
succeeded in prolonging the network lifetime. Nevertheless, some
of those approaches have focused on one side and have ignored the
others, which have crucial eects on power consumption. For ex-
ample, some approaches have caused additional delay and increase
in the complexity as [
9
]. Others have resulted in an increase in the
overhead ratio as [
15
] and [
11
]. In addition, some other approaches
have target dense networks while sparsely distributed sensor nodes
with small-sized clusters might be the case in many applications
[
2
] and [
14
]. Finally, some methods have both considered both the
Lifetime Enhancement of WSN Based on Improved LEACH with Cluster Head Alternative Gateway ICFNDS ’20, November 26–27, 2020, St.Petersburg, Russian Federation
distance and residual energy metrics to select the optimal next hop,
which is the CH of the next hope cluster. Consequently, the CH
nodes suer from high congestion [1].
In this work, an enhanced version of LEACH is proposed in a try
to prolong the network lifetime by reducing the burden on the CH
node. This is achieved through selecting an alternative gateway in
the cluster level on the basis of residual energy and the distance to
the cluster. Consequently, instead of randomized selections, guided
selections are applied to prolong the network lifetime.
3 ASSUMPTIONS
One of the main factors that aect the lifetime of WSN is the node
deployment method. Here are some important assumptions:
•LEACH protocol is used for routing missions.
•Several nodes are distributed into a specic area.
•Each cluster consists of random number of nodes.
•Each node has an ID, coordinates, and initial energy.
•
CHs are located randomly without any consideration of the
position of the node. That means the CHs that are near to
the BS will consume less energy than the far ones.
•
The node battery is not rechargeable so that the node dies if
the energy of its battery is consumed.
4 PROPOSED APPROACH
The drawbacks of the LEACH protocol could be briefed in three
basic problems. First of all, the residual energy is not taken into
account when CH selection is performed. It depends more on ran-
domized choices. Secondly, the distance to the base station is not
taken into consideration. Finally, LEACH works properly and ef-
fectively with a small environment. However, it shows a shortage
with large environments.
Our proposed approach uses two measurements to overcome
the previously mentioned drawbacks. The two measurements are
the distance to the BS and the residual energy, which overcome the
downside of the I-LEACH [
2
] approach, which does not take the
distance to the BS into consideration. Also, our proposed approach
supports small and large clusters as will be explained subsequently
in section 4.
Finally, the HEED protocol requires several iterations to con-
struct the cluster, and so it has higher overhead and higher energy
consumption [
15
]. In order to avoid the drawbacks of the HEED
protocol, the proposed approach in this paper forms the clusters
in the same manner of the original LEACH, which demands fewer
iterations than the HEED. The proposed approach introduces a
method to overcome the previously mentioned drawbacks. The key
idea of this approach is to elect the Alternative Gateway (AG) node,
which has the highest residual energy. The AG election process
is done on a cluster-level. Also, the distance to the base station is
taken into consideration. Hence, a choice will be taken to steer the
trac to the closest gateway, (here by gateway, we mean either CH,
AG, or BS).
The main steps of the suggested algorithm are as follows:
(1) Clusters construction.
(2) Random CH election.
(3) AG election due to the residual energy.
(4) Path selection and trac steering.
Figure 1: Pseudo-Code of our proposed approach
4.1 Alternative Gateway Selection
The key point of the proposed approach is the way how AG node
is selected. After CHs election and clusters construction, the AG
election takes place. On cluster-level; the node, which holds the
highest residual energy is elected as an AG node.
After the Alternative Gateway (AG) election process is done,
the distance from the CH to the BS and from the CH to the AG
is calculated. Thus, a comparison between the two distances is
done by the CH to select the shortest path. Finally, the CH’s trac
is steered to the closest gateway; here by gateway (gateway here
means either CH, AG, or BS). The pseudo-code for the proposed
approach is shown in Figure1.
4.2 Path Selection and Trac Steering
In order to shed more light on the how the proposed method selects
the path on which the trac is steered, the following cases shows
how the proposed method fulls this task:
•Case1:
This case supposes the distance from the CH to the BS
is the shortest. Hence, the CH’s trac is forwarded directly
to the BS . Accordingly, the amount of consumed energy is
ICFNDS ’20, November 26–27, 2020, St.Petersburg, Russian Federation Abdel-Nasser et al.,
reduced if compared with the other path when the trac
forwarded to the AG node, as shown in Figure 2. In this
case, the proposed approach has no eect on the amount of
residual energy because it wasn’t selected as the best path.
Figure 2: Case1
•Case2:
This case supposes the distance from the CH to the
AG node is the shortest. Hence, the CH’s trac is forwarded
to the AG node instead of the BS to save more energy, as
shown in Figure 3. Actually, this case overcomes the draw-
back of ignorance of the distance to the BS in LEACH pro-
tocol. Due to the far distance to the BS, it is clear that the
amount of consumed energy would be bigger if the trac
forwarded directly to the BS.
Figure 3: Case2
•Case3:
This case supposes that there are two AG nodes
away from the CH the same distance, which also less than
the distance to the BS, as shown in Figure 4. In this case,
the trac of the CH is forwarded to any of the AG nodes,
because they have the same distance to the CH. This case also
achieves the purpose of the proposed approach by reducing
the distance and thus the power.
Figure 4: Case3
•Case4:
This case supposes the distance from the CH to the
AG node, and from the CH to the BS are equal , as shown
in Figure 5. In this case, the trac is forwarded to the BS
directly. However, it is better for the AG node that the trac
is steered directly to the BS, due to the unlimited lifetime
the BS has, if compared with the AG node.
Figure 5: Case4
5 RESULTS AND ANALYSIS
Our proposed algorithm has been evaluated in terms of the to-
tal residual energy of the sensor nodes, the number of overhead
packets, and the overhead size.
5.1 Simulation Environment
Our algorithm has been implemented on top of the Network Sim-
ulator (NS2) [
13
] with the help of the Mannasim framework [
7
].
The Mannasim framework is a module for WSN simulation based
Lifetime Enhancement of WSN Based on Improved LEACH with Cluster Head Alternative Gateway ICFNDS ’20, November 26–27, 2020, St.Petersburg, Russian Federation
on the (NS2). Mannasim extends NS2 introducing new modules for
design, development, and analysis of dierent WSN applications.
NS2 is a discreet event simulator targeted at networking research
and provides substantial support for simulation of routing, multi-
cast protocols and IP protocols, such as UDP, TCP, RTP, and SRM
over wired and wireless (local and satellite) networks. It has many
advantages that make it a useful tool, such as support for multiple
protocols and the capability of graphically detailing network traf-
c. Additionally, NS2 supports several algorithms in routing and
queuing [6].
The experiments are performed with a diverse number of nodes
placed in a 100 m
×
100 m area. All nodes have the same initial
energy of 10 joules. The BS is located in (50,50). The simulation
parameters used are shown in Table 1.
Parameter Settings
Simulation Area 100m*100m
Number of Nodes 10,30,50,70,100
Channel Type Wireless Channel
Antenna Model Omni Antenna
Energy Model Battery
Initial Energy Model 10 joules
Base Station Position (50,50)
Maximum Value 30
Routing Protocol LEACH
Transport protocol UDP
Type of Mac 802_11
Radio Propagation Two Ray Ground
Common Node Location Random
Simulation Time 10 s
Interface Queue Type DropTail
Link Layer Type LL
Table 1: Simulation Basic Congurations.
5.2 Experimental Results
We have created 5 network topologies randomly using the simula-
tion to evaluate our proposed algorithm. Each benchmark has dif-
ferent number of nodes ranges between 10,30,50,70 and 100 nodes.
As shown in Table 2, after comparing between the original and
modied algorithms in terms of the total residual energy, it has
been found that the enhanced algorithm increases the average of
total residual energy by 4.35% compared with the original LEACH.
The eectiveness of the modied algorithm is demonstrated in
terms of increasing the residual energy through steering the trac
to the closest gateway. Figure 6 shows the enhancement in the
residual energy when the number of nodes increases.
In this modied algorithm, the number of overhead packets
and the sum of all overhead packets’ sizes are expected to increase.
Figure 7 shows that by increasing the number of nodes, the overhead
size and ratio are also increasing because of the additional packets
that are added to the overall network as a result of electing AGs.
The increase in overhead values is inevitable, because the pro-
cess of electing AG looks like electing a CH. Hence, the process
of electing a new AG and joining to an AG will roughly consume
the same number and size of overhead packets. However, there are
critical measures that control the overhead values and the level of
consumed energy, which are the distance to the BS, the way how
CH is elected, network scale, and cluster density. Hence, only using
the distance to the BS is not enough to control the overhead ratio.
In addition, the mentioned measurements aect the overhead ratio
more than the distance lonely as justied in [9] [14].
6 CONCLUSION AND FUTURE WORK
In this paper, with the research of the LEACH protocol, we put
forward a novel node called Alternative Gateway (AG). This en-
hanced algorithm has overcome the defects of the original LEACH
by taking the node’s residual energy and fairness to the base station
(BS) into consideration. The proposed algorithm outperforms the
original LEACH by 4.35% saving in the average of consumed energy,
which prolongs the network lifetime.
As future work, we are looking to achieve a load balancing con-
cept by considering the threshold. Besides, the Alternative Gateway
(AG) cannot serve the network in all its lifetime, therefore, electing
another AG node is demanded to achieve a load balancing concept.
Finally, we will consider analyzing and improving the computa-
tional complexity of the proposed protocol.
REFERENCES
[1]
Maman Abdurohman, Yadi Supriadi, and Fitra Zul Fahmi. 2020. A Modied
E-LEACH Routing Protocol for Improving the Lifetime of a Wireless Sensor
Network. Journal of Information Processing Systems 16 (08 2020), 845–858. https:
//doi.org/10.3745/JIPS.03.0142
[2]
Z. Beiranvand, A. Patooghy, and M. Fazeli. 2013. I-LEACH: An ecient routing
algorithm to improve performance to reduce energy consumption in Wireless
Sensor Networks. (2013), 13–18.
[3]
Mohamed Elshrkawey, Samiha M Elsherif, and M Elsayed Wahed. 2018. An
enhancement approach for reducing the energy consumption in wireless sensor
networks. Journal of King Saud University-Computer and Information Sciences 30,
2 (2018), 259–267.
[4]
S. Thangavelu G. Anitha, V. Vijayakumari. 2018. A Comprehensive Study
and Analysis of LEACH and HEED Routing Protocols for Wireless Sensor Net-
works–with Suggestion for Improvements. Indonesian Journal of Electrical Engi-
neering and Computer Science 9. https://doi.org/10.11591/ijeecs.v9.i3.pp778-783
[5]
Luis Javier García Villalba, Ana Lucila Sandoval Orozco, Alicia Trivino Cabrera,
and Claudia Jacy Barenco Abbas. 2009. Routing protocols in wireless sensor
networks. Sensors 9, 11 (2009), 8399–8421.
[6]
Ibrahim F Haddad and David Gordon. 2002. Network simulator 2: a simulation
tool for linux. Linux Journal 10 (2002).
[7]
Linnyer Beatryz Ruiz José Marcos Silva Nogueira. 2020 (accessed August 30,
2020). The Mannasim Framework. http://www.mannasim.dcc.ufmg.br/
[8]
Hu Junping, Jin Yuhui, and Dou Liang. 2008. A time-based cluster-head selection
algorithm for LEACH. In 2008 IEEE Symposium on Computers and Communications.
IEEE, 1172–1176.
[9]
Ji-Zhen Long, Yuan-Tao Chen, Dong-Mei Deng, LI Bin, and LI Fang. 2011. As-
sistant cluster head clustering algorithm based on LEACH protocol. Computer
engineering 37, 7 (2011), 103–105.
[10]
A. Nandi, B. Sonowal, D. Rabha, and A. Vaibhav. 2019. Centered Sink LEACH
Protocol for Enhanced Performance of Wireless Sensor Network. In 2019 Inter-
national Conference on Automation, Computational and Technology Management
(ICACTM). 436–440. https://doi.org/10.1109/ICACTM.2019.8776765
[11]
Hicham Ouldzira, Hajar Lagraini, Ahmed Mouhsen, Mostafa Chhiba, and
Tabyaoui Abdelmoumen. 2019. MG-leach: an enhanced leach protocol for wire-
less sensor network. International Journal of Electrical and Computer Engineering
(IJECE) 9 (08 2019), 3139. https://doi.org/10.11591/ijece.v9i4.pp3139-3145
[12]
Raju Pal, Subash Yadav, Rishabh Karnwal, et al
.
2020. EEWC: energy-ecient
weighted clustering method based on genetic algorithm for HWSNs. Complex &
Intelligent Systems (2020), 1–10.
[13]
Ekram Hossain Teerawat Issariyakul. 2012. The NS2 Framework. https://www.
springer.com/gp/book/9781461414056
ICFNDS ’20, November 26–27, 2020, St.Petersburg, Russian Federation Abdel-Nasser et al.,
Benchmark Number of Nodes Residual Energy of Original Residual Energy of Modied Improvement %
1 10 1.171 J 1.214 J 4.3%
2 30 1.145 J 1.197 J 5.2%
3 50 1.116 J 1.163 J 4.7%
4 70 1.152 J 1.186 J 3.4%
5 100 1.193 J 1.235 J 4.15%
Avg 1.1554 J 1.1989 J 4.35%
Table 2: Total residual energy (in Joules) in original and proposed algorithm versus the number of nodes.
Figure 6: Total residual energy (in Joules) in original and proposed algorithm versus the number of nodes.
Figure 7: The number of overhead packets and overhead size (in Bytes) in original and proposed algorithm versus the number
of nodes.
[14]
Mu Tong and Minghao Tang. 2010. LEACH-B: an improved LEACH protocol
for wireless sensor network. In 2010 6th international conference on wireless
communications networking and mobile computing (WiCOM). IEEE, 1–4.
[15]
Abdulhamid Zahedi. 2018. An ecient clustering method using weighting
coecients in homogeneous wireless sensor networks. Alexandria Engineering
Journal 57, 2 (2018), 695–710.
