Energy-Efficient Cluster Head Selection Using Hybrid Sandpiper Competitive Swarm Optimization In Wireless Sensor Networks
DOI:
https://doi.org/10.29284/5b1a5r43Keywords:
Wireless Sensor Networks (WSNs), Energy Efficiency, Cluster Head Selection, Sandpiper Optimization Algorithm (SOA), Competitive Swarm Optimization (CSO), Network Lifetime, Swarm Intelligence, IoT.Abstract
Wireless Sensor Networks (WSNs) consist of numerous low-power sensor nodes deployed to monitor and collect environmental data. Energy conservation and improving the network life remain significant challenges due to limited battery capacity and high communication overhead. Clustering techniques, when combined with intelligent optimization algorithms, can significantly reduce energy consumption. However, conventional optimization methods often struggle to balance exploration and exploitation phases effectively. To address this limitation, this paper proposes a hybrid Sandpiper Optimization Algorithm–Competitive Swarm Optimization (SOA-CSO) technique for efficient Cluster Head (CH) selection. The hybrid algorithm leverages the global search capability of Sandpiper Optimization Algorithm and the competitive learning dynamics of Competitive Swarm Optimization to achieve optimal Cluster Head selection. Simulation results show that the proposed SOA-CSO achieves significant improvement in network life time, throughput, packet delivery ratio, load balance and network stability compared to the other algorithm.
References
1. M. Musa, "WSNs for Precision Agriculture," Sensors, vol. 24, p. 51, 2023.
2. S. Tabatabaei, "New Energy Efficient Management Approach for Wireless Sensor Networks," ScienceDirect, 2025.
3. T.A. Abose, "Improving Wireless Sensor Network Lifespan with Optimized Clustering," ScienceDirect, 2024.
4. Y. Qu, "Intelligent Unequal Clustering in Wireless Sensor Networks," Nature Communications, 2025.
5. M.A. Alqarni, "Improved Wireless Sensor Network Data Collection Using Optimization Algorithms," ScienceDirect, 2023.
6. H. Khujamatov, "Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimizer," Sensors, vol. 24, no. 13, p. 4406, 2024.
7. M.K. Roberts, "An Improved Dual-Phased Meta-Heuristic Optimization for Energy-Efficient Routing in Wireless Sensor Networks," ScienceDirect, 2024.
8. Yadav, R.K., Mahapatra, R.P., & Sahoo, S.K. "Hybrid Metaheuristic Algorithm for Optimal Cluster Head Selection in WSNs," Pervasive and Mobile Computing, 2022.
9. J. Pan, "Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with Gannet Optimization and Differential Evolution," MDPI, 2023.
10. S. Zhang, "Improving the Functionality of Wireless Sensor Networks Using Meta-Heuristic Algorithms," Nature Communications, 2025.
11. Kumar, S., Kumar, R., & Kumar, V. “Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm.” Sustainable Energy Technologies and Assessments, 52, 102243, 2022.
12. Sankar, S., Ramasubbareddy, S., & Dhanaraj, R.K. “Cluster head selection for the Internet of Things using a Sandpiper Optimization Algorithm.” Journal of Sensors, 2023.
13. Patel, F., & Kiani, F. “Chaotic Sand Cat Swarm Optimization: A new hybrid metaheuristic algorithm for constrained and complex optimization problems.” Mathematics, 11(5), 2023.
14. Somula, R., Cho, Y., & Mohanta, B.K. “EACH-COA: An energy-aware cluster head selection for the Internet of Things using the Coati Optimization Algorithm.” 2023.
15. Rajesh Kumar, R., & Dhanaraj, R.K. “Enhanced security-based energy-aware routing for RPL in IoT network.” Manipal University, 2024.
16. Yang, L., Zhang, S., & Wang, X. “Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree.” Scientific Reports, 14, 2024.
17. Gupta, A., & Ramasubbareddy, S. “Sandpiper Optimization Algorithm with cosine similarity-based cross-layer routing protocol for smart agriculture in WSN assisted Internet of Things systems.” 2025.
18. Nirmala, S., & Dhanaraj, R.K. “Energy-efficient cluster head selection in Internet of Things networks using an optimized evaporation rate Water-Cycle Algorithm.” Springer Open Journal of Electrical Engineering & Technology, 2025.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
