A Novel Cluster Head Selection Algorithm to Maximize Wireless Sensor Network Lifespan

International Journal of Computer Networks & Communications (IJCNC) Vol.17, No.1, January 2025
DOI: 10.5121/ijcnc.2025.17108 121
A NOVEL CLUSTER HEAD SELECTION ALGORITHM
TO MAXIMIZE WIRELESS SENSOR
NETWORK LIFESPAN
Almamoon Alauthman 1
, Wan NorShuhadah Wan Nik 2
1
Electrical Engineering Department, Al-Balqa Applied University, As Salt 19117, Jordan
2
Faculty of Informatics and Computing, University Sultan Zainal Abidin,
Terengganu, Malaysia
ABSTRACT
Wireless Sensor Networks (WSNs) are crucial for various applications such as environmental monitoring,
industrial automation, and healthcare. However, the constrained energy resources of sensor nodes have a
substantial effect on the longevity and performance of these networks. To address this issue, this paper
introduces the Optimized Energy Efficient algorithm in Wireless Sensor Networks through Cluster Head
Selection Using Residual Energy and Distance Metrics together. The study offers a new approach to
selecting cluster heads by combining residual energy and distance metrics. The proposed algorithm called
modified intelligent energy efficiency cluster algorithm (MIEEC-A), that enhances WSN energy efficiency
by choosing nodes with high residual energy and close proximity to their neighbours as cluster heads.
Extensive simulations and evaluations show that this approach effectively extends network lifetime,
improves data aggregation, and boosts energy efficiency, thus making a valuable contribution to WSN
lifetime.
KEYWORDS
WSN, Cluster Head, Cluster Distance, Residual Energy, Sensor Node, BCE-LEACH, IEEC-A, OEE-WCRD
1. INTRODUCTION
WSNs are widely used in today's world, spanning various fields such as healthcare, industry, and
military applications. The WSNs consists of a large number of sensors that are powered by
batteries. The sensor nodes gather, process, aggregate, compress, and send data to the base
station, all requiring efficient energy usage. The overall lifespan of the WSN can be impacted by
how well energy efficiency is managed [1]. Several studies have been published about ways to
reduce the power consumption of wireless sensors to maximize their lifetime. Insufficient power
supply is a critical issue for WSNs, often leading to network failures due to node malfunctions
[2]. As a result, WSNs are highly energy-sensitive, unlike other wireless networks that are not as
dependent on power. Since each sensor node transmits data directly to the base station, its battery
is rapidly drained, leading to power depletion. To maximize the network's lifespan and enhance
its performance, efficient energy use is crucial. Consequently, the clustering approach has been
implemented to lower energy consumption, extend the network's lifetime, and boost throughput.
Since direct data transfer from the cluster to the base station consumes more energy, each cluster
is assigned a Cluster Head (CH) [3]. Routing is a critical element in WSNs to prolong the
network's lifespan. A routing protocol that utilizes the clustering technique is the most efficient
way to maximize network longevity by reducing the power consumption of sensor nodes [4]. A
LEACH clustering routing protocol is one of the more power-efficient clustering protocols,
which divides a WSN's sensors into multiple clusters. The LEACH protocol operates on a

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clustering mechanism, where sensors transmit their collected data to Cluster Heads (CHs) at the
start of each round. Each CH is then responsible for forwarding the aggregated data to the Base
Station (BS). It does not take into account the remaining energy of the sensor when selecting CHs
at random. Additionally, a sensor's power consumption increases directly dueto communication
between the Cluster Head (CH) and the Base Station (BS). In turn, this will reduce the lifespan of
the wireless sensor network. Due to these reasons, we developed an improved energy efficient
clustering technique based on the LEACH protocol in order to maximize network lifetime by
reducing energy consumption. Many metrics that can be used to compare different routing
protocols in WSNs. The performance of a protocol is primarily influenced by factors such as
residual energy, communication distances, multi-hop processes, and network lifespan. [5].
The widespread deployment of WSNs in fields such as environmental monitoring, healthcare,
and industrial automation has introduced significant challenges related to energy efficiency and
network longevity. Due to the resource constraints of sensor nodes, often powered by limited-
capacity batteries, innovative solutions are needed to prolong network lifetime while maintaining
efficient data communication. One promising approach to address this issue is the use of cluster-
based algorithms within WSNs [6]. These algorithms have attracted significant interest due to
their effectiveness in reducing energy consumption and enhancing network scalability. They
achieve this by organizing sensor nodes into clusters and selecting a cluster head to aggregate and
transmit data from the member nodes to a central sink or gateway. By minimizing the number of
nodes actively transmitting data, these algorithms decrease energy overhead and extend the
network's operational lifespan [7].
The paper is organized as follows: Section 2 outlines the motivation for the research. Section 3
reviews related work. Section 4 provides a detailed explanation of the proposed algorithm.
Section 5 presents the results and analysis. Finally, Section 6 offers a conclusion, summarizing
the findings and exploring future research opportunities.
2. MOTIVATION AND CONTRIBUTION
This research is driven by the critical need to enhance the energy efficiency and overall
performance of Wireless Sensor Networks (WSNs), which are essential in sectors like
environmental monitoring, healthcare, and industrial automation. While current cluster-based
methods have demonstrated promise in reducing energy consumption, there is a growing need for
novel approaches to further optimize these networks [8].
Our research responds to this need by proposing an innovative cluster-based algorithm designed
to greatly improve energy efficiency in WSNs. By strategically grouping sensor nodes into
clusters and employing dynamic cluster reconfiguration techniques, our algorithm not only
prolongs the network's operational lifespan but also maintains data integrity and enhances
coverage quality. This positions it as a noteworthy advancement in the field of cluster-based
WSNs.
The manuscript is structured as follows: The introduction provides an overview of the importance
of cluster-based WSNs and outlines the research objectives. The subsequent section offers a
comprehensive review of existing cluster-based techniques and their limitations. Next, we
provide an in-depth description of our proposed cluster-based algorithm, covering its design,
functionality, and adaptability to changing network conditions. The experimental methodology
section outlines the setup and metrics employed to assess the algorithm's performance. Finally,
the results and discussion section presents the empirical findings and their significance. Finally,
we conclude with a summary of our contributions, emphasizing the practical significance of our

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novel cluster-based approach in enhancing energy efficiency and network longevity in WSNs,
and suggest avenues for future research.
3. RELATED WORK
In Wireless Sensor Networks (WSNs), challenges include limited sensor battery life, harsh
environmental conditions, node heterogeneity, Quality of Service (QoS) requirements, node
security, and ensuring fully functional deployments [9]. To keep the network operational and
extend the lifespan of WSNs, various multi-constrained routing mechanisms have been
developed in the literature. Among these, cluster-based routing is the most commonly used
approach for achieving efficient communication by optimizing resource usage. This method also
enhances node management, load balancing, and scalability [10].Given the various challenges in
WSNs, the primary goal of researchers is to minimize energy consumption and ensure efficient
data transmission. Various hierarchical cluster-based routing protocols have been developed to
improve energy efficiency and prolong the network's lifespan by considering factors such as
energy consumption, distance, coverage, mobility, and node density. Numerous algorithms have
been proposed in the literature to address energy efficiency issues in specific areas [11].Ongoing
research is focused on integrating various approaches and creating hybrid models that further
improve energy efficiency and extend the network's lifespan. MODLEACH [12] It is a variation
of the LEACH protocol that uses an energy threshold for efficient cluster head formation. If the
current cluster head's energy exceeds the threshold, it remains in its role for the next round.
However, if its energy falls below the threshold, it is replaced following the original LEACH
algorithm. CRPD [13] is a cluster-based routing protocol designed for dynamic wireless sensor
networks to reduce energy consumption and enhance energy efficiency by updating the network
topology.
The results of the proposed mechanism are compared with existing models that effectively
address similar challenges. Several of these protocols are discussed in this paper, including BCE-
LEACH [14], which is an improved version of the Low Energy Adaptive Hierarchy (LEACH)
protocol, known as Balanced Current Energy-LEACH (BCE-LEACH). This improved algorithm
is designed to extend the lifespan of network sensor nodes by balancing their power usage. It
selects Cluster Heads (CHs) based on the remaining power of the sensor nodes, allowing only
those with adequate remaining energy to qualify as CHs. MO-LEACH [15], this protocol
suggests that CHs can forward data to help reduce energy consumption if one is positioned
centrally along the path. In this protocol, data transmission from CH to BS is optimized to
conserve energy, but as the distance from the base station increases, adjacent sensor nodes
experience higher energy consumption. IEEC-A[16] is a redesigned clustering protocol
techniquefor WSNs to maximize their lifetime and throughput. The proposed clustering
technique minimizes power consumption by selecting the optimal Cluster Head (CH) with the
highest remaining power. It also emphasizes the importance of choosing the most suitable route
for transmitting data between the CH and the base station. This technique considers the energy
remaining in the nodes when selecting the CH and determines the distance between the sensor
and the centerof the cluster. OEE-WCRD [17] is an algorithm designed to enhance energy
efficiency in Wireless Sensor Networks by selecting Cluster Heads based on residual energy and
distance metrics.We proposed a modified Intelligent Energy Efficient Clustering Approach
algorithm, named MIEEC-A, aimed at enhancing energy-efficient cluster head selection to
prolong the lifespan of wireless sensor networks. This algorithm is designed to optimize energy
usage and enhance network longevity by employing an improved cluster head selection strategy.
It effectively addresses the dynamic nature of nodes and resolves the issue of fixed round times
for changing cluster heads. In this approach, the cluster head selection process considers the
residual energy of sensor nodes, their mobility, the number of nodes associated with them, and

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the distance of the nodes from the base station. The cluster formation is structured so that when a
node moves within the network, it can easily join a new cluster based on its new position while
leaving its previous cluster without disrupting it. If the moving node is a cluster head, it selects a
new cluster head from within its cluster before departing. This approach helps preserve the
integrity of the cluster when the cluster head changes. Table 1 presents a summary of this
literature review.
Table 1: Features and Challenges of Existing Work
Year Parameter for CH election Inter Cluster Routing Cluster Count
[18] 2019  Residual Energy Single-hop / Multi-hop Fixed
[19] 2019
 Residual Energy
 CH Count
 Distance from BS
Single-hop / Multi-hop Variable
[20] 2018
 Residual Energy
 Largest Node Degree
Single-hop / Multi-hop Fixed
[21] 2018
 Residual Energy
 Distance from BS
 Distance from cluster
centre
[22] 2018  Random Selection Multi-Hop Fixed
[23] 2016  Position based Single-hop Fixed
[24] 2017
 Residual Energy
 Number of Neighbour
[25] 2017  Probability Single-hop / Multi-hop Fixed
[26] 2020
 Residual Energy
 CH Count
 Distance from BS Single-hop / Multi-hop
Fixed
[27] 2021
 Residual Energy
 Distance from clusters
node Single-hop / Multi-hop
Fixed
4. PROPOSED METHODOLOGY
The proposed OEE-WCRD protocol selects the cluster head (CH) based on two parameters: the
residual energy of the node and the average distance from the node to the base station.
4.1. Residual Energy Evaluation
Evaluate the residual energy of each sensor node in the network using available energy
estimation methods, such as monitoring battery voltage or current consumption [28]. Assign a
weight to each node based on its remaining energy, with nodes having higher energy levels being
regarded as more suitable candidates for cluster head selection.
4.2. Distance Metric
Once a cluster head (CH) is selected, data must be transmitted to the base station (BS) via a root
path. The direct transmission between CHs and the BS involves a relatively long distance, which
results in significant energy consumption. To address this, the proposed clustering method uses a

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shortest path algorithm, similar to [29], to minimize energy use. The algorithm chooses one of
the Cluster Heads (CHs) along the path based on the number of connected sensors. The
current node seeks a neighboring CH with fewer connections, repeating the process until the
neighboring CH with the fewest sensors becomes the new cluster head. This continues until the
BS is reached. Once all paths from CH to BS are determined, the BS calculates the shortest hop
path to each CH and communicates the optimal route to them for future transmissions.
4.3. Combining Residual Energy and Distance
Develop a selection criterion that integrates residual energy and distance metrics to prioritize
potential cluster head nodes. For instance, a weighted scoring system could rank nodes according
to their remaining energy and their distance from other nodes. Set a threshold or ranking limit to
determine the candidates for cluster head selection.
4.4. Cluster Head Election
Elect cluster heads from the eligible nodes based on the selection criteria and ranking. Ideally,
cluster heads should have sufficient residual energy and be near cluster members for efficient
data aggregation. A weight function is defined as follows in the point function [30]:
Point (P) = E × V +
(1 − 𝑉)
𝐷2
(1)
In this equation, E represents the remaining energy of the sensor node within the cluster,
X denotes the weight, and D is the distance between the sensor node and the cluster
center. The cluster CH is determined by choosing the node with the highest point (P) in each
cluster. The value of X ranges between 0 and 1, with a typical value around 0.85, as over 50% of
total energy consumption occurs during data transmission between sensors.
4.5. Clustering
4.5.1. Cluster Creation
After selecting the cluster heads, create clusters by assigning nearby sensor nodes to their
respective cluster heads. The distance metric is used to determine which sensor nodes should be
assigned to each cluster.
4.5.2. Cluster Size Management
Introduce a mechanism to regulate the size of each cluster, ensuring balanced energy
consumption. Use dynamic resizing or merging of clusters to enhance network performance.
4.5.3. Data Aggregation
Set up communication links between cluster members and their cluster heads. Utilize the cluster
head nodes for aggregating data and transmitting it to the base station or sink.
4.5.4. Cluster Head Rotation

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Periodically rotate cluster heads to evenly distribute energy consumption across the network and
prevent the early depletion of specific nodes.
4.5.5. Network Dynamics Adaptation
Create algorithms or guidelines to adjust the clustering structure based on changing network
conditions, such as node failures or mobility.
Pseudo-code for the proposed algorithm (MIEEC-A)
1 sensors = ini_sensors()
2 clusters = cluster_network(sensors)
3 highest_energy_sensor = None
4 if highest_energy_sensor is None or sensor.energy>highest_energy_sensor.energy:
5 highest_energy_sensor = sensor
6 else if sensor.energy == highest_energy_sensor.energy:
7 if distance(sensor.location, cluster.center) < distance(highest_energy_sensor.location,
cluster.center):
9 cluster.head = highest_energy_sensor
10 while true:
11 if sensor.energy< threshold:
12 clusters = cluster_network(sensors)
13 break
14 if cluster.head.energy< threshold:
15 highest_energy_sensor = None
16 if sensor.energy>thr and (hi_en_sensor is None or sen.en>hi_en_sen.energy):
18 if highest_energy_sensor is not None:
19 cluster.head = highest_energy_sensor
5. RESULT
This section presents the simulation results that assess the performance of the proposed clustering
algorithm using the NS3 simulation tool. The proposed algorithm is compared with LEACH,
BCE LEACH, and MO-LEACH protocols to demonstrate its effectiveness in maximizing
network lifetime and throughput. Table 2 provides the detailed simulation parameters used in the
experiments.
Table 2: Simulation Parameter
Number of node 500
Initial energy 25 J
Energy of data aggregation 50nj/bit
Packet size 1500 bit
Radio module exhausts energy 50nj/bit
Area 250 X 250

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As stated in [30], network lifetime refers to the period during which the deployed sensors can
monitor the target phenomenon. One way to measure this is by tracking the time when the first
sensor depletes its power and ceases to function. This is an important metric for assessing how
well a network can prolong its sensing period [31]. In Figure 1, the number of dead nodes within
the network is compared. LEACH sees all sensors die by round 6000, BCE-LEACH by round
6300, IEEC-A by round 6800, andOEE-WCRD by round 6900, whereas the proposed clustering
algorithm MIEEC-A extends sensor life until round 7000. These results indicate that the
proposed technique effectively maximizes network lifetime, as intended by the study.
Figure1. The total number of dead nodes in the network.
In the following simulation, the time is measured from when the network starts collecting data
until the first sensor fails. As indicated in Table 3, the number of sensors ranges from 100 to
1000.
Table 3: Time until the sensor Fails
No. OF
Sensor
LEACH
BCE-
LEACH
OEE-
WCRD
IEEC-A MIEEC-A
100 9.1 14 19 19 20
300 7 12 15.3 15.2 16.1
500 5.6 9.5 13 12.9 13.5
700 4.5 7.2 10.8 10.5 11.3
1000 3.2 5 8.1 8 8.7
Figure 2 displays the time until the first sensor fails in a network environment with 100 sensors.
The LEACH protocol results in sensor failure in 9.1 seconds, BCE-LEACH in 14 seconds, IEEC-
A in 19 seconds, OEE-WCRD in 19 seconds, and the proposed algorithm MIEEC-A in 20
seconds.

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Figure2. Time until the first sensor fails in a network environment with 100 sensors.
In the third simulation, the number of messages sent from the cluster heads (CHs) to the base
station is calculated as a function of throughput. Table 4 presents the throughput for four existing
protocols—LEACH, BCE-LEACH, IEEC-A,and OEE-WCRD, and compares them with the
proposed clustering algorithm MIEEC-A.
Table 4: Throughput Comparison
ROUND LEACH
BCE-
LEACH
OEE-
WCRD
IEEC-E
MIEEC-
E
100 4030 4030 4030 4030 4030
300 11770 12001 12102 12055 12096
500 15420 18980 19980 19930 20409
700 15840 23460 25618 25730 27118
900 16100 26400 28491 28400 30711
1000 16120 26930 30053 29260 31806
Figure 3 displays the throughput for a network running for 400Sensor with varying numbers of
Rounds. The LEACH protocol has the lowest throughput because most sensors fail after 500
rounds. BCE-LEACH shows improved throughput at 1000 rounds, approximately twice as good
as LEACH. However, both ICCE-A and OEE-WCRD demonstrate superior performance, with
OEE-WCRD showing slightly better throughput than IEEC-A.The proposed algorithm MIEEC-A
clearly shows that it’s the best performance than other techniques regarding throughput. This
improvement is attributed to the shortest path algorithm implemented in the proposed technique.

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Figure 3. Throughput for a network running for 400 Sensor with varying numbers of Rounds.
Figure 4 illustrates the objective of the fourth simulation, which provides an overview of energy
consumption among network nodes using the LEACH, BCE-LEACH, IEEC-A, OEE-WCRD,
and proposed clustering techniques MIEEC-A. The energy consumption rates differ among these
protocols. The LEACH curve shows linearity for 950 rounds, BCE-LEACH for 1600 rounds,
IEEC-A for 4710, OEE-WCRD for 4820 rounds, and the proposed technique for 4900 rounds.
The results indicate that LEACH's total node energy drops to nearly zero after 3500 iterations,
BCE-LEACH maintains energy up to 5000 iterations, while IEEC-A and OEE-WCRD sustain
energy until 5100 iterations, and the proposed technique until 5500 iteration. These findings
support the claim that the proposed approach delivers better performance compared to
conventional methods.
Figure4. Energy consumption among network nodes.
Furthermore, it can improve the longevity, throughput, and overall lifespan of the wireless sensor
network (WSN) compared to existing protocols. As a result, the proposed clustering technique
extends the WSN's lifetime more effectively than other mentioned protocols.

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The final simulation, conducted over 1000 rounds with varying numbers of sensors as shown in
Figure 5, demonstrates that the proposed clustering technique achieves the highest throughput
compared to the LEACH, BCE-LEACH, IEEC-A, and OEE-WCRD protocols.
Figure5. Throughput for a network running for 1000 rounds with varying numbers of sensors.
6. CONCLUSION
This paper introduces a redesigned clustering head selection for wireless sensor networks
(WSNs), that isaimed at maximizing both their lifetime and throughput. The proposed technique
reduces power consumption by selecting the optimal cluster head (CH) with the highest available
power and determining the most efficient route for data transfer between the CH and the base
station. In contrast to LEACH, which selects Cluster Heads (CHs) randomly, the proposed
method takes into account the energy levels of the nodes and the distance between the sensor and
the cluster center when selecting the CH. It also optimizes the routing path for data transmission
using a multi-hop approach to minimize power consumption. The study compares the
performance of the proposed clustering technique with three recent protocols—LEACH, BCE-
LEACH, IEEC-A, and OEE-WCRD protocols. Simulation results show that the proposed
technique lowers power consumption in CH selection and data transmission, which in turn
enhances the WSN's lifespan and boosts throughput. Additionally, it selects the most appropriate
node as the CH, resulting in fewer dead nodes compared to existing protocols. The proposed
technique also demonstrates superior throughput compared to LEACH, BCE-LEACH, IEEC-A,
and OEE-WCRD. Future research will aim to incorporate advanced machine learning and
artificial intelligence techniques to improve cluster head selection and routing decisions, further
boosting energy efficiency and adaptability.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGMENTS
The authors would like to thank everyone, just everyone!

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AUTHORS
Almamoon Alauthman earned his Bachelor's degree in Computer Engineering from
Al-Balqa Applied University (BAU) in Amman, Jordan, and his Master's degree in
Computer Engineering from Jordan University of Science and Technology (JUST) in
Irbid, Jordan. He completed his Ph.D. at Sultan Zainal Abidin University (UniSA),
Malaysia. Currently, he serves as an assistant professor at the Faculty of Engineering
(Electrical Engineering Department) at Al-Balqa Applied University. His research
interests include VLSI design, parallel processing, neural networks, computer
architecture and organization, and wireless networks.
Wan Nor Shuhadah Wan Nik is currently a senior lecturer at the Faculty of
Informatics and Computing, University Sultan Zainal Abidin (UniSA), Malaysia. She
earned her Ph.D. in Computer Science (Distributed Systems) from the University of
Sydney, Australia, in 2012. From 2014 to 2017, she served as the Deputy Director
(Infrastructure & Services) at the Information Technology Centre, UniSA. Dr. Wan Nik
has been involved in over ten research grants and has led four national grants in the field
of Distributed Systems. Her primary research interests include Computer Networks,
Distributed Systems, Scheduling in Grid/Cloud and Utility Computing, Wireless Sensor
Networks, IoT, Heuristics and Optimization, and Blockchain.

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