C4O: chain-based cooperative clustering using coati optimization algorithm in WSN

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 13, No. 1, March 2024, pp. 96~104
ISSN: 2089-4864, DOI: 10.11591/ijres.v13.i1.pp96-104  96
Journal homepage: http://ijres.iaescore.com
C4O: chain-based cooperative clustering using coati
optimization algorithm in WSN
Preet Kamal Singh1
, Harmeet Singh1
, Jaspreet Kaur2
1
Department of Computer Science and Engineering, CT University, Punjab, India
2
Department of Computer Science and Engineering, Gulzar Group of Institutions, Punjab, India
Article Info ABSTRACT
Article history:
Received Apr 9, 2023
Revised Sep 1, 2023
Accepted Sep 13, 2023
In order to provide sensing services to low-powered IoT devices, wireless
sensor networks (WSNs) organize specialized transducers into networks.
Energy usage is one of the most important design concerns in WSN because
it is very hard to replace or recharge the batteries in sensor nodes. For an
energy-constrained network, the clustering technique is crucial in preserving
battery life. By strategically selecting a cluster head (CH), a network's load
can be balanced, resulting in decreased energy usage and extended system life.
Although clustering has been predominantly used in the literature, the concept
of chain-based clustering has not yet been explored. As a result, in this paper,
we employ a chain-based clustering architecture for data dissemination in the
network. Furthermore, for CH selection, we employ the coati optimisation
algorithm, which was recently proposed and has demonstrated significant
improvement over other optimization algorithms. In this method, the
parameters considered for selecting the CH are energy, node density, distance,
and the network’s average energy. The simulation results show tremendous
improvement over the competitive cluster-based routing algorithms in the
context of network lifetime, stability period (first node dead), transmission
rate, and the network's power reserves.
Keywords:
C4O
Cluster head
Optimization algorithm
Routing
Wireless sensor network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Preet Kamal Singh
Department of Computer Science and Engineering, CT University
Sidhwan Khurd, Punjab, India
Email: preetkamal17317@ctuniversity.in
1. INTRODUCTION
Wireless sensor networks (WSNs) are designed with a self-operated, small, and widely scattered
mechanism for detecting certain limitations [1], [2], which aids in the transit of data through the network to
sink. If all of the nodes in a WSN have the same capabilities, we call it a homogeneous WSN; otherwise, we
call it a heterogeneous WSN [3]. The WSN can be used in a wide variety of fields, including medicine,
transportation, the military, industry, the environment, and agriculture. Considering how much power is used
during internal sensor node communication, it is crucial to think of a routing technique that could help the
sensor nodes conserve power [4], [5].
Clustering, also known as cluster analysis, is a technique used to organise large amounts of sensor
data into manageable groups based on their shared properties [6]. A critical function of the cluster head (CH)
is to collect information from the cluster's nodes and forward it to the cluster's sink [7]. Power efficiency and
system longevity are prioritised during CH estimate in WSN. Several criteria are used to determine the
CH [8], [9]. The ideal CH is determined by optimising the number of nearest neighbours, the distance from the
sink, and the amount of energy that is left over. The lifespan of WSN is also affected by other factors, but the
choice of CH is especially crucial [10]. So, selecting the appropriate CH is critical for enhancing the

Int J Reconfigurable & Embedded Syst ISSN: 2089-4864 
C4O: chain-based cooperative clustering using coati optimization algorithm in WSN (Preet Kamal Singh)
97
performance of the network as a whole. In the search for the best CH, numerous meta-heuristic approaches
have been developed. They include particle swarm optimization (PSO), genetic algorithm (GA), and ant colony
optimization (ACO). Yet, it is challenging to provide accurate estimates of real-time difficulties in terms of all
relevant criteria [11], [12].
In contrast to conventional CH selection approaches, the proposed work uses coati optimization
algorithm (COA) [13]. The COA's exploitation capabilities converge rapidly, and the agency's exploring
efficiency is top notch. The proposed approach effectively takes advantage of the COA's exploitation behaviour
after convergence. So, the proposed technique is not constrained to a merely local optimal solution, but can
instead discover the global optimal one. Further, COA conducts a global search for a better solution. The most
important contribution of the proposed work is as:
− A chain-based clustering architecture is proposed for data dissemination in the network as illustrated in
Figure 1.
− Furthermore, for CH selection, we employ the COA, which was recently proposed and has demonstrated
significant improvement over other optimisation algorithms.
− In this method, the parameters considered for selecting the CH are energy, node density, distance, and
network's average energy.
− The simulation results show tremendous improvement over the competitive cluster-based routing
algorithms in the context of network lifetime, stability period (first node dead), throughput, and network’s
remaining energy.
In section 2 of this work, we give a literature review on the topic of CHs in WSN. In section 3, we
present a model for how a WSN chooses its cluster leader. In section 4, we analyse the results and explore what
they mean. Our conclusion and future directions are presented in section 5.
Normal Nodes
Cluster Head
Advanced
nodes
Super nodes
Sink
Figure 1. Proposed architecture of COA
2. LITERATURE REVIEW
Various researchers have focused the problem of CH selection. Here are some of the important
contributions of the researchers [14]–[16]. By incorporating fuzzy logic into WSN, Baradaran and Navi [17]
have introduced high-quality clustering algorithm (HQCA) and optimized CHs in 2020. The HQCA approach
was utilised as a criterion for increasing the intra-cluster and inter-cluster distances and decreasing the error
rate during clustering. Fuzzy logic was used together with other factors such as the distances between cluster
nodes and the base station (BS), cluster node energies, and residual node energies to determine the best CH.

 ISSN: 2089-4864
Int J Reconfigurable & Embedded Syst, Vol. 13, No. 1, March 2024: 96-104
98
Ultimately, the experimental results using the proposed method achieved great reliability and a reduced error
rate than those obtained using more conventional methods.
Sahoo et al. [18] proposed the PSO-based energy efficient clustering and sink mobility (PSO-ECSM)
method for CHs in WSN in 2020. Both the sink movement problem and the CHs issue were addressed by the
suggested PSO-ECSM, and comprehensive simulations were run to evaluate its effectiveness. Using
transmitted data in a multi-hop network and the PSO-ECSM algorithm with a mobile sink, authors compared
alternative values for five criteria. Finally, when compared to more conventional models, the adopted
approach's performance has shown considerable improvements in network lifetime, throughput, and stable
period.
A GA-based optimum CHs for both multi and solitary data sinks in heterogeneous WSN was proposed
in 2019 by Verma et al. [19]. Because of the limitations of density, remaining energy, and range, the GA-based
optimized clustering (GAOC) protocol was developed for optimal CHs. In addition, multiple data sinks based-
GAOC (MS-GAOC) has analysed the hotspot problems and reduced the communicative lengths between the
nodes and sink. The proposed MS-GAOC was put to the test empirically, and the results showed that it
outperformed the other algorithms.
The misbehavior detection approach and the secure CH selection algorithm for clustering WSN were
proposed by Ghawy et al. [20]. It was based on the trust management strategy for clustering WSN. Moreover,
the issue of selecting the reliable node as CH was solved. The SNs' actions served as a benchmark by which
the monitoring plan was measured. Finally, testing results have proven that the adopted approach is superior
in protecting the network from compromised nodes becoming CHs.
To facilitate clustering in WSNs, Priya et al. [21] presented a hybrid energy management approach in
2020. The authors have proposed a new method based on Lagrangian relaxation and entropy to achieve energy
efficiency. In addition, the approach has been kept alive by changing the position for the multi-hop
connectivity. In the end, simulation results showed that the adopted model performed better than the baseline.
2.1. Inferences drawn from the literature work
In this section, we discuss inferences that we have identified from the existing work:
− The selection of CH has been the topic of concern handled by the various researchers [22], [23].
− Meta-heuristic approaches have been considered as there are multiple parameters that decides its
selection; hence, the effective fitness function is computed by using various optimization methods.
− Since, these optimization methods have their own merits and demerits, therefore selecting the suitable
optimization method becomes a crucial task [24].
− As it is evident from the literature survey, PSO has been rigorously used for clustering as it has faster
convergence in terms of delivering the solution [25].
− However, it is weak in exploration capabilities. Whereas, COA has better exploration capabilities than
PSO.
2.2. Background of coatis
Coatis, or coati mundis, belong to the procyonidae family and are found in the nasua and nasuella
genera. All coatis have the same slim body, long, non-prehensile tail used for signalling and balance, black
paws, small ears, and a long, flexible, expanded nose. Coatis reach lengths of 33–69 cm (13–27 inches) from
snout to tail tip at adulthood. The average coati weighs between 2 and 8 kg, and they stand about 30 cm at the
shoulder. The average adult male can grow to be almost twice as large as a female, and they have larger, more
pointed canine teeth. You can use these dimensions to compare a South American coati to a white-nosed coati.
The smaller of the two coati species is the mountain coati. Coatis are omnivores that consume a wide variety
of foods, including invertebrates like tarantula and small vertebrate prey like birds, lizards, rodents, crocodile
eggs, and bird eggs. The green iguana is one of the coati's favourite foods. Since these enormous lizards
(iguanas) prefer to spend their time in the trees, coatis often band together to kill them. Some coatis may climb
trees in an attempt to intimidate the iguana into jumping to the ground, while others will immediately launch
themselves into an attack. Nonetheless, coatis are vulnerable to attacks from a variety of predators. Coatis
employ clever planning in their attacks on iguanas, and they exhibit cunning in the face of and flight from
predators. The proposed COA method was largely inspired by the simulation of these wild coatis' behaviour.
3. PROPOSED WORK
3.1. Network presumptions
This model takes the network concept into account while making a few assumptions. The following
assumptions are made about the sensor nodes:

99
− Each and every one of the network's nodes is a permanent fixture. These low-cost, low-size nodes collect
data and relay it to the sink.
− It is assumed that the nodes in this network have varying degrees of energy, making the network
heterogeneous. This protocol takes into account nodes at the normal, moderate, and advanced levels of
energy.
− Once the energy in the nodes is depleted, there is no way to replenish it.
− A square of area A=MXM nodes is used for deployment. The sink is positioned smack dab in the midst
of the system.
− As soon as the nodes are deployed, they are each given a unique identifier.
− The wireless data transmission security concerns are outside the scope of this paper.
3.2. Operating phases of proposed work
3.2.1. Set up phase
The proposed method is used to pick the CH at this stage. The network's nodes are a mixture of
different types. There are three tiers of heterogeneity built into the network, and this results in nodes with
varying amounts of available energy [26]. NNOR, NINT, and NHGH stand for the respective numbers of low-
energy, medium-energy, and high-energy nodes in the network, respectively, in the (1)-(9). The percentage of
nodes with medium and high energy levels is denoted by the values λ1 and µ1, respectively.
NNOR = n × λ1 (1)
NINTI = n × µ1 (2)
NHIGH = n × (1 − λ1 − µ1) (3)
The energy of the intermediate and high energy nodes is twice that of the low energy nodes,
respectively. Following this procedure, which is also denoted by ETot, the total energy of the network can be
calculated. Higher node energy EHGH, intermediate node energy EINTI, and normal node energy ENORM, are all
energy values.
EHGH = Enrm × (1 + λ1) × n × λ11 (4)
EINT = Enrm × (1 + µ1) × n × µ11 (5)
ENORM = Enrm × (1 − λ1 − µ1) × n (6)
ETot = EADVN + ESUP + ENORM (7)
ETot = Enrm × (1 + λ1) × n × λ11 + Enrm × (1 + µ1) × n × µ11 + Enrm ×
(1 − λ1 − µ1) × n (8)
ETot = n × Enrm × (1 + λ × λ11 + µ × µ11) (9)
For example, in (1)-(9), the symbols 𝜆1 and µ1 represent the proportion of nodes that are intermediate
and "advanced" respectively. Additionally, 𝜆11 and µ11 represent the energy portions of the aforementioned
nodes. The entire network's energy is calculated for inclusion in the fitness function, which is used to define
the reasons that led to the selection of CH.
3.3. Fitness function for C4O
A fitness function is an expression that can be optimized by increasing or decreasing the value of some
set of performance parameters. The fitness of a person is determined by a number of factors, and these aspects
are taken into account by the computed fitness function. The fitness function makes use of the following
arguments.
3.3.1. Fitness parameters
The present value of the FP is determined using a complex formula. The more weight a parameter has,
the better the ideal value will be. Here, efficiency in energy consumption and durability in the network are
prioritized as fitness factors. The following criteria are considered throughout the fitness function design
process.

 ISSN: 2089-4864
100
a) The remaining/left energy of a node
The efficiency parameter chooses CH based on the sensor node's power leftover. Since the sensor
nodes' energy is depleted over the course of a round, the residual energy of those nodes must be tracked in
order to choose one as CH. The fitness factor is introduced by the first fitness parameter (FP1st) in the following
fashion.
FP1st =
1
E(i)
(10)
In (10), since the goal is to reduce the efficiency variable by choosing the node with the highest remaining
energy, the ith
sensor node's remaining energy is included in the denominator term.
b) Distance between node and sink
After being randomly distributed around the network, the nodes' distances from the sink also fluctuate.
Lower energy usage can be achieved by minimizing the distance between the sensor nodes and the sink. This
makes it an important consideration when choosing a CH. In order to save power, the distance between the
cluster nodes as well as between cluster centers and the sink should be as small as possible. This is calculated
using the Euclidean distance formula, which uses the coordinates of the two objects as inputs.
As revealed by (11), the second fitness parameter (FP2nd) is concerned with the creation of the fitness
function to gain the selection of CH via the distance parameter.
FP2nd = ∑ (
N
i=1
D(N(i)−Sink)
DAVG(N(i)−Sink)
) (11)
For each node, FP2nd totals the distance costs, where i is an integer from 1 to N𝑇 (the total number of nodes).
In (11), DN(i)−Sink represents the average distance between the ith
node and the sink, and DAVG(N(i)−Sink)
represents the Euclidean distance between the ith
node and the sink. It's important to remember that the lower
this parameter's value is, the better the network's CH selection will be.
c) Node density
Therefore, the choosing of CH is performed according to the number of surrounding nodes, as it is
vital to minimise the distance between the cluster nodes and the CH. This is done by finding the nodes that are
the least connected to one another. The number of nearby nodes is defined by the third fitness parameter (FP3rd)
in (12):
FP3rd = (
∑ D(Nd(i)−Nd(j))
NCL
i=1,j=1
NCLUS
) (12)
for the purpose of computing cluster member nodes, the preceding (12) uses the notation D(Nd(i)−Nd(j)) for the
Euclidean distance between each pair of nodes in the cluster. The NCLUS variable represents the total number
of cluster nodes. This means that FP3rd needs to be kept low if the CH is to be an efficient user of energy.
d) Network’s average value of energy
Given how important it is to minimize the average energy between cluster nodes and the CH, the CH
is chosen using this metric. The fourth fitness parameter (FP4tℎ) deals with average energy of the network and
is computed by (13).
FP4th =
1
N𝑇
∑
1
E(i)
N𝑇
1=1 (13)
In (13), E(𝑖) stands for the energy of the ith
node in the network, and N𝑇 stands for sum total of the network's
nodes. Thus, maximizing FP4th is necessary for optimal CH selection.
3.3.2. The optimisation process and its fitness function
It is important to keep in mind that the fitness function is calculated by combining several variables
into a single unified expression, as shown in (14).
F =
1
α1×FP1st+β1×FP2nd+γ1×FP3rd+Ὠ1×FP4th
(14)

101
In order to achieve maximum efficiency, set the fitness function in (14) to its smallest value. Weight values
considered along with fitness parameters are denoted by, α1, β1, γ1, Ὠ1 and in (15). It is important to notice
that all of these parameters are assigned values on the same scale. According to (15), the sum of these weights
is 1.
α1 + β1 + γ1 + Ὠ1 = 1 (15)
3.3.3. Steady state phase
As immediately as a CH is chosen using hybrid suggested research, the nodes' tunicates, position and
velocity processes are updated. After that, in the data sending phase, packet data transfer continues. All the
nodes submit their data to the CH node, which aggregates it and then sends it to the sink.
4. SIMULATION RESULTS
The simulation results are given in Figures 2-6. The performance comparison of proposed work is
given in Figure 3. It is evident from the results shown that the proposed work i.e., C4O outperforms the other
protocols in first node dead as well as for different percentage of nodes dead.
Each node's energy consumption during communications with other nodes and the data collection sink
is calculated by using a mapping of the radio energy model [26]. The nodes begin using energy in accordance
with the energy model as soon as the second phase, the data transmission phase, begins. A sensor node's energy
consumption increases linearly with the square of its distance. Therefore, distance is an important consideration
in determining the final energy level of the nodes in a network that have received messages.
It is evident from the Figures 3-6 and from Table 1, the proposed work has shown improvement in the
network lifetime and at various stages of dead nodes. The stability or the first node dead is improved by 104.1%
as compared to particle swarm optimization (PSO)-based dual sink mobility (PSODSM) protocols. Further,
the 75% node dead achieved at 142% improvement over PSODSM protocol. The reason for such improvement
for the proposed work is due to the optimized choosing the CH and also the use of hybrid optimization method
that helps in providing the network the optimized solution at the faster rate.
Dynamic CH-GA (DCH-GA), Genetic algorithm based energy efficient clusters (GABEEC), Genetic algorithm based distance aware-
low-energy adaptive clustering hierarchy (GADA-LEACH)
Figure 2. Performance comparison
3,608 4,390 4,400 5,825
8,607
17,601
7,883
10,642
9,337
12,885
18,245
44,073
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
DCH_GA GABEEC GADA_LEACH GAOC PSODSM C4O
Performance Comparison
1st Node Dead 10 % Node Dead 30 % Node Dead
50 % Node Dead 75 % Node Dead

 ISSN: 2089-4864
102
Figure 3. Remaining energy of proposed work Figure 4. Alive node status of proposed work
Figure 5. Dead node status of proposed work Figure 6. Throughput status of proposed work
Table 1. Performance comparison of proposed work
Parameter DCH-GA GABEEC GADA-LEACH GAOC PSODSM C4O
1st node dead 3,608 4,390 4,400 5,825 8,607 17,601
10% node dead 5,777 5,739 5,225 9,726 10,435 29,778
30% node dead 6,616 8,500 7,356 10,458 13,578 33,584
50% node dead 7,111 9,086 7,722 10,674 14,684 36,123
75% node dead 7,883 10,642 9,337 12,885 18,245 44,073
5. CONCLUSION
The primary focus of WSNs research is on increasing energy efficiency and extending network
lifetime. To handle this concern, various researchers have proposed multitude routing algorithms. It has been
observed that the CH selection is a non polynomial (NP)-hard problem and seeks serious attention. In this
paper, the CH selection is proposed using the COA algorithm. The simulations have been performed in
MATLAB software and it is evident from the outcomes that the proposed algorithm has not only improved
lifetime but also the stability period of the network tremendously. The network performance is improved in the
context of the throughput and network’ residual energy expenditure. The reason behind such improvement are
as follows; high convergence and large exploration due to the COA, the CH selection parameters i.e., energy,
distance, neighbouring nodes and the network’s average energy.

103
However, there are some limitations of the proposed work which can be addressed in the future.
Firstly, the assumptions of the physical medium put a lot of challenges that need to be handled for real time
implementation. Further, the security feature should be addressed as the wireless communication is vulnerable
to various attacks. Lastly, the sink mobility can be considered for checking the performance of proposed work
under different use case.
REFERENCES
[1] S. Verma, S. Zeadally, S. Kaur, and A. K. Sharma, “Intelligent and secure clustering in wireless sensor network (wsn)-based
intelligent transportation systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 13473–13481, Aug.
2022, doi: 10.1109/TITS.2021.3124730.
[2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Communications Magazine,
vol. 40, no. 8, pp. 102–114, Aug. 2002, doi: 10.1109/MCOM.2002.1024422.
[3] S. Tyagi and N. Kumar, “A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor
networks,” Journal of Network and Computer Applications, vol. 36, no. 2, pp. 623–645, Mar. 2013, doi: 10.1016/j.jnca.2012.12.001.
[4] F. A. Alaba, M. Othman, I. A. T. Hashem, and F. Alotaibi, “Internet of things security: a survey,” Journal of Network and Computer
Applications, vol. 88, pp. 10–28, Jun. 2017, doi: 10.1016/j.jnca.2017.04.002.
[5] M. Perillo, Z. Cheng, and W. Heinzelman, “An analysis of strategies for mitigating the sensor network hot spot problem,” in The
Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, San Diego, CA, USA:
IEEE, 2005, pp. 474–478, doi: 10.1109/MOBIQUITOUS.2005.10.
[6] S. Verma, S. Kaur, M. A. Khan, and P. S. Sehdev, “Toward green communication in 6G-enabled massive internet of things,” IEEE
Internet of Things Journal, vol. 8, no. 7, pp. 5408–5415, Apr. 2021, doi: 10.1109/JIOT.2020.3038804.
[7] A. I. Al-Sulaifanie, B. K. Al-Sulaifanie, and S. Biswas, “Recent trends in clustering algorithms for wireless sensor networks: a
comprehensive review,” Computer Communications, vol. 191, pp. 395–424, Jul. 2022, doi: 10.1016/j.comcom.2022.05.006.
[8] P.-Y. Kong, “Distributed sensor clustering using artificial neural network with local information,” IEEE Internet of Things Journal,
vol. 9, no. 21, pp. 21851–21861, Nov. 2022, doi: 10.1109/JIOT.2022.3181596.
[9] A. Lipare, D. R. Edla, and R. Dharavath, “Fuzzy rule generation using modified PSO for clustering in wireless sensor networks,”
IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 846–857, Jun. 2021, doi:
10.1109/TGCN.2021.3060324.
[10] N. Ma, H. Zhang, H. Hu, and Y. Qin, “ESCVAD: an energy-saving routing protocol based on voronoi adaptive clustering for
wireless sensor networks,” IEEE Internet of Things Journal, vol. 9, no. 11, pp. 9071–9085, Jun. 2022, doi:
10.1109/JIOT.2021.3120744.
[11] H. Ali, U. U. Tariq, M. Hussain, L. Lu, J. Panneerselvam, and X. Zhai, “ARSH-FATI: a novel metaheuristic for cluster head
selection in wireless sensor networks,” IEEE Systems Journal, vol. 15, no. 2, pp. 2386–2397, Jun. 2021, doi:
10.1109/JSYST.2020.2986811.
[12] C. Chen, L.-C. Wang, and C.-M. Yu, “D2CRP: A novel distributed 2-hop cluster routing protocol for wireless sensor networks,”
IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19575–19588, Oct. 2022, doi: 10.1109/JIOT.2022.3148106.
[13] M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, “Coati optimization algorithm: a new bio-inspired metaheuristic
algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 259, p. 110011, Jan. 2023, doi:
10.1016/j.knosys.2022.110011.
[14] R. K. Yadav and R. P. Mahapatra, “Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network,”
Pervasive and Mobile Computing, vol. 79, p. 101504, Jan. 2022, doi: 10.1016/j.pmcj.2021.101504.
[15] S. Hriez, S. Almajali, H. Elgala, M. Ayyash, and H. B. Salameh, “A novel trust-aware and energy-aware clustering method that
uses stochastic fractal search in iot-enabled wireless sensor networks,” IEEE Systems Journal, vol. 16, no. 2, pp. 2693–2704, Jun.
2022, doi: 10.1109/JSYST.2021.3065323.
[16] S. Karimi-Bidhendi, J. Guo, and H. Jafarkhani, “Energy-efficient node deployment in heterogeneous two-tier wireless sensor
networks with limited communication range,” IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 40–55, Jan. 2021,
doi: 10.1109/TWC.2020.3023065.
[17] A. A. Baradaran and K. Navi, “HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic
in wireless sensor networks,” Fuzzy Sets and Systems, vol. 389, pp. 114–144, Jun. 2020, doi: 10.1016/j.fss.2019.11.015.
[18] B. M. Sahoo, T. Amgoth, and H. M. Pandey, “Particle swarm optimization based energy efficient clustering and sink mobility in
heterogeneous wireless sensor network,” Ad Hoc Networks, vol. 106, p. 102237, Sep. 2020, doi: 10.1016/j.adhoc.2020.102237.
[19] S. Verma, N. Sood, and A. K. Sharma, “Genetic algorithm-based optimized cluster head selection for single and multiple data sinks
in heterogeneous wireless sensor network,” Applied Soft Computing, vol. 85, p. 105788, Dec. 2019, doi:
10.1016/j.asoc.2019.105788.
[20] M. Z. Ghawy et al., “An effective wireless sensor network routing protocol based on particle swarm optimization algorithm,”
Wireless Communications and Mobile Computing, vol. 2022, p. e8455065, May 2022, doi: 10.1155/2022/8455065.
[21] A. V. Priya, A. K. Srivastava, and V. Arun, “Hybrid optimal energy management for clustering in wireless sensor network,”
Computers and Electrical Engineering, vol. 86, p. 106708, Sep. 2020, doi: 10.1016/j.compeleceng.2020.106708.
[22] B. Zhu, E. Bedeer, H. H. Nguyen, R. Barton, and J. Henry, “Improved soft-k-means clustering algorithm for balancing energy
consumption in wireless sensor networks,” IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4868–4881, Mar. 2021, doi:
10.1109/JIOT.2020.3031272.
[23] M. A. U. Rehman, R. Ullah, B.-S. Kim, B. Nour, and S. Mastorakis, “CCIC-WSN: an architecture for single-channel cluster-based
information-centric wireless sensor networks,” IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7661–7675, May 2021, doi:
10.1109/JIOT.2020.3041096.
[24] J. Liu, P. Tong, X. Wang, B. Bai, and H. Dai, “UAV-aided data collection for information freshness in wireless sensor networks,”
IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2368–2382, Apr. 2021, doi: 10.1109/TWC.2020.3041750.
[25] H. Yu and W. Xiaohui, “PSO-based energy-balanced double cluster-heads clustering routing for wireless sensor networks,”
Procedia Engineering, vol. 15, pp. 3073–3077, 2011, doi: 10.1016/j.proeng.2011.08.576.
[26] D. Pant, S. Verma, and P. Dhuliya, “A study on disaster detection and management using WSN in Himalayan region of
Uttarakhand,” in 2017 3rd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall),
Dehradun, India: IEEE, Sep. 2017, pp. 1–6, doi: 10.1109/ICACCAF.2017.8344703.

 ISSN: 2089-4864
104
BIOGRAPHIES OF AUTHORS
Preet Kamal Singh completed bachelors of technology from SLIET Longowal in
2012. After than he completed his M.Tech. from PAU Ludhiana in 2014. He is currently
pursuing his Ph.D. from CT University Ludhiana. His research interest includes wireless sensor
network. He can be contacted at email: preetkamal17317@ctuniversity.in.
Harmeet Singh is working as assistant professor and deputy director IPR Cell at
CT University, Ludhiana, India. He got his bachelors and master’s degree in computer
applications. Then Ph.D. in bio-robotics from Scuola Superiore Sant’ Anna University, Italy.
His current research interests are in signal processing and machine learning for autonomous
systems and human-machine interaction; bio signal driven intelligent systems and healthcare.
He can be contacted at email: harmeet17333@ctuniversity.in.
Jaspreet Kaur is currently working as professor and dean, Department of CSE at
Gulzar group of Institutions, Ludhiana. Prior to this she worked with CT University, from August
2017-December 2022 as coordinator of School of Engineering and Technology. She can be
contacted at email: jaspreet.kaur@ggi.ac.in.

C4O: chain-based cooperative clustering using coati optimization algorithm in WSN

Recommended

More Related Content

Similar to C4O: chain-based cooperative clustering using coati optimization algorithm in WSN (20)

More from International Journal of Reconfigurable and Embedded Systems (20)

Recently uploaded (20)

C4O: chain-based cooperative clustering using coati optimization algorithm in WSN