Leveraging Chaotic Wind-Driven Optimization with Equilibrium Optimization Algorithm for Cluster-Based Routing in WSN
Leveraging Chaotic Wind-Driven Optimization with Equilibrium Optimization Algorithm for Cluster-Based Routing in WSN |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Author : Sambu Anitha, T. Suresh, V. Sathiyasuntharam |
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| DOI : 10.14445/22315381/IJETT-V73I4P124 | ||
How to Cite?
Sambu Anitha, T. Suresh, V. Sathiyasuntharam, "Leveraging Chaotic Wind-Driven Optimization with Equilibrium Optimization Algorithm for Cluster-Based Routing in WSN," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp.279-291, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P124
Abstract
Wireless Sensor Networks (WSNs) contain many spatially spread sensor nodes linked over the wireless standard to observe and trace the physical data from the location. Generally, the WSN nodes are battery-driven; therefore, they will lose whole energy after a definite time. This kind of energy restriction leads to the lifetime of the system. The objective is to diminish the complete energy utilization and boost the networking lifespan. Routing and clustering techniques are commonly employed in WSNs to improve the lifespan. The aim is to mitigate the energy utilization of the sensor nodes throughout data transmission. This upsurges the total packet spread to BS by lowering the sensor nodes' energy utilization. This study generally employs swarm intelligence due to its searching capability, self-adaptability, and robustness. This article proposes the Chaotic Wind Driven with Equilibrium Optimization Algorithm for Efficient Cluster-based Routing (CWDEO-ECBR) technique in WSN. The CWDEO-ECBR technique utilizes the concept of clustering with a route selection process to enhance the network efficiency. The CWDEO-ECBR technique comprises two significant phases of operations. Initially, the CWDEO-ECBR technique uses a chaotic wind-driven optimization (CWDO) technique for selecting the cluster heads (CHs) and organizing clusters. In the second stage, the CWDEO-ECBR technique employs an equilibrium optimizer (EO) method for the routing process. A comprehensive simulation analysis is conducted to evaluate the performance of the CWDEO-ECBR approach. The CWDEO-ECBR model achieved a superior accuracy of 99.54% in NOAN, highlighting its efficiency in improving WSN network performance compared to existing methods.
Keywords
Wireless Sensor Network, Equilibrium Optimizer, Clustering, Routing, Cluster Head, Chaotic Wind-Driven Optimization.
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