Abstract

IJLTEMAS

International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)

IJLTEMAS

2278-2540

IJLTEMAS

10.51583/IJLTEMAS.2026.150700008

Network

An Efficient Multi-Cluster Wireless Sensor Network Based on Optimal Cluster Head Selection Using Particle Swarm Optimization

Kamil

Ismaila

Department of Electrical and Electronic Engineering, Osun State University, Osogbo, Nigeria Nigeria Adeleke Adetokun

Goodness

Department of Electrical and Electronic Engineering, Osun State University, Osogbo, Nigeria Nigeria

15 7 98 111 19 07 2026 24 07 2026

05 08 2026

Wireless Sensor Network (WSN); cluster head selection; particle swarm optimization (PSO); energy efficiency; multi-clustering; network lifetime.

Abstract

Wireless sensor networks (WSNs) are crucial in various scientific, industrial and infrastructure monitoring applications, in which the sensing, processing, and communication tasks are performed by the distributed sensors, without the presence of any human beings. WSNs have various drawbacks, among which is the power limitation of sensor nodes, which impacts the life of the network and its reliability, due to their battery power. The main problem is the selection of cluster heads (CHs) as CH selection in conventional protocols may be ad hoc/Random which may cause energy imbalance and network failure, which is very common in conventional protocols. The main idea of this study is to present a multi-cluster WSN model with optimal CH selection (the MO model) in which the CHs are selected by a particle swarm optimization (PSO) algorithm, by solving a multipurpose optimization problem based on three criteria: residual energy, distance between candidate CHs and the base station, and intra-cluster distance. The proposed MO model is tested against three baseline models: single-cluster arbitrary selection (SA), multi-cluster arbitrary selection (MA), and single-cluster optimal selection (SO) and tested over 50, 100 and 200 network sizes. Residual energy, number of alive nodes, total energy consumption, network stability and PSO convergence rate are used to evaluate the performance. In all three sizes of the network, the MO model always consumes less energy than the baseline models (up to 52% in the three networks at 200 nodes, when the number of alive nodes is half that of the MO model at equivalent simulation rounds) and significantly outperforms all three baseline models in terms of normalized size stability (up to 1.00 in the three networks at 100 nodes, compared with at most 0.30 for the baseline models). The obtained results indicate that multi-clustering and joint optimization of CH selection, taking into account multiple energy relevant criteria yields substantial, stable gains in energy-efficiency and life-time of the WSN in comparison to arbitrary CH selection and a single criterion CH selection scheme.

References 1

R. A. Mohamed, M. M. Hassan, and A. M. Ali, “Architecture and applications of wireless sensor networks: A review,” in Proc. IEEE Int. Conf. Sensor Networks, 2018, pp. 1–6.

H. Amrieen, T. Aljarrah, and M. Al-Smadi, “Clustering techniques for energy efficiency in wireless sensor networks: A review,” J. Netw. Comput. Appl., vol. 152, 2020, Art. no. 102511, doi: 10.1016/j.jnca.2019.102511.

W. A. Altakhayneh, M. Ismail, M. A. Altahrawi, and M. K. Abu Foul, “Cluster head selection using genetic algorithm in wireless sensor network,” in Proc. 14th Int. Conf. Malaysia-Indonesia Commun. (MICC), 2019, pp. 13–18.

K. Kavitha, S. Rahul, and R. Gururaj, “Energy efficient data management in wireless sensor networks,” in Proc. IEEE Int. Conf. Commun. Signal Process., 2019, pp. 201–206.

Y. Han, X. Li, and J. Zhao, “TDMA-based data aggregation scheduling for cluster-based wireless sensor networks,” IEEE Sensors J., vol. 20, no. 14, pp. 7891–7901, 2020, doi: 10.1109/JSEN.2020.2980732.

A. Lipare and D. R. Edla, “Cluster head selection and cluster construction using fuzzy logic in WSN,” in Proc. IEEE Region 10 Conf. (TENCON), 2019, pp. 1–6.

A. Shahraki, A. Taherkordi, Ø. Haugen, and F. Eliassen, “A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms,” IEEE Trans. Netw. Service Manag., vol. 18, no. 2, pp. 2242–2274, 2021, doi: 10.1109/TNSM.2020.3035315.

S. S. Saleh, T. F. Mabrouk, and R. A. Tarabishi, “An improved energy-efficient head election protocol for clustering techniques of wireless sensor network,” Egyptian Informat. J., vol. 22, no. 4, pp. 439–445, 2021, doi: 10.1016/j.eij.2021.01.003.

J. Wang, J. Niu, K. Wang, and W. Liu, “An energy-efficient fuzzy cluster head selection algorithm for WSNs,” IEEE Access, vol. 8, pp. 153657–153666, 2020, doi: 10.1109/ACCESS.2020.3017864.

V. A. Adtya, P. L. Sashrika, K. K. Shivani, and T. Jayanth, “Closeness-centrality-based cluster head selection algorithm for large-scale WSNs,” in Proc. 12th Int. Conf. Comput. Intell. Commun. Netw. (CICN), 2020, pp. 107–111.

P. Rawat and S. Chauhan, “Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network,” Neural Comput. Appl., vol. 33, no. 21, pp. 14147–14165, 2021, doi: 10.1007/s00521-021-06059-7.

RCH-LEACH: Residual Energy based Cluster Head Selection in LEACH for Wireless Sensor Networks, in Proc. IEEE 11th Int. Conf. Comput. Commun. Netw. Technol. (ICCCNT), 2020, pp. 1–6, doi: 10.1109/ICCCNT49239.2020.9225637.

A. Kumar, M. Sharma, and R. Gupta, “A comprehensive survey on LEACH-based clustering routing protocols in wireless sensor networks,” Ad Hoc Netw., vol. 110, 2021, Art. no. 102374, doi: 10.1016/j.adhoc.2020.102374.

J. S. Raj, M. Robinson, and L. Vijayalakshmi, “A survey on cluster head selection and cluster formation methods in wireless sensor networks,” Wireless Commun. Mobile Comput., vol. 2022, Art. no. 5322649, 2022, doi: 10.1155/2022/5322649.

S. Sennan, S. Ramasubbareddy, S. Balasubramaniyam, A. Nayyar, M. Abouhawwash, and N. A. Hikal, “Energy-efficient cluster head selection in wireless sensor networks-based Internet of Things using fuzzy-based Harris Hawks optimization,” Telecommun. Syst., vol. 86, pp. 1–18, 2024, doi: 10.1007/s11235-024-01176-9.

H. Hu, X. Fan, and C. Wang, “Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks,” Sci. Rep., vol. 14, Art. no. 18595, 2024, doi: 10.1038/s41598-024-69360-0.

Z. Zhao, Y. Liu, and X. Chen, “A distributed particle-swarm-optimization-based fuzzy clustering protocol for wireless sensor networks,” Sensors, vol. 23, no. 15, Art. no. 6699, 2023, doi: 10.3390/s23156699.

V. Prakash and S. Pandey, “Metaheuristic algorithm for energy-efficient clustering scheme in wireless sensor networks,” Microprocess. Microsyst., vol. 101, 2023, Art. no. 104898, doi: 10.1016/j.micpro.2023.104898.

T. Luo, H. Wang, and Y. Li, “An improved Lévy chaotic particle swarm optimization algorithm for energy-efficient cluster routing scheme in industrial wireless sensor networks,” Expert Syst. Appl., vol. 224, 2023, Art. no. 122780, doi: 10.1016/j.eswa.2023.122780.

S. Singh, D. Garg, and A. Malik, “Energy-optimization route and cluster head selection using M-PSO and GA in wireless sensor networks,” Wireless Pers. Commun., vol. 136, pp. 1–22, 2024, doi: 10.1007/s11277-024-11096-1.

R. Somula, Y. Cho, and B. K. Mohanta, “SWARAM: Osprey optimization algorithm-based energy-efficient cluster head selection for wireless sensor network-based Internet of Things,” Sensors, vol. 24, no. 2, Art. no. 521, 2024, doi: 10.3390/s24020521.

P. C. S. Rao, P. Lalwani, H. Banka, and G. S. Rao, “Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks,” Multimed. Tools Appl., vol. 80, pp. 26093–26119, 2021, doi: 10.1007/s11042-021-10901-4.

A. Baradaran and F. Rabieefar, “NEECH: A new energy-efficient clustering and routing algorithm for wireless sensor networks,” Comput. Netw., vol. 224, 2023, Art. no. 109618, doi: 10.1016/j.comnet.2023.109618.

N. Malisetti and V. K. Pamula, “Performance of quasi-oppositional butterfly optimization algorithm for cluster head selection in WSN,” Procedia Comput. Sci., vol. 171, pp. 1954–1960, 2020, doi: 10.1016/j.procs.2020.04.210.

M. M. Aroba, N. Naicker, and T. Adeliyi, “An innovative hyperheuristic, Gaussian clustering scheme for energy-efficient optimization in wireless sensor networks,” J. Sensors, vol. 2021, Art. no. 6666742, 2021, doi: 10.1155/2021/6666742.