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Energy Aware Dung Beetle Optimization-Based Clustering Scheme for Wireless Sensor Networks

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 6
Year of Publication : 2024
Authors : K. Nirmal, S. Murugan
10.14445/23488549/IJECE-V11I6P105
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How to Cite?

K. Nirmal, S. Murugan, "Energy Aware Dung Beetle Optimization-Based Clustering Scheme for Wireless Sensor Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 6, pp. 53-62, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I6P105

Abstract:

Wireless Sensor Networks (WSNs) have acquired considerable interest owing to their widespread applications in habitat tracking, healthcare, agriculture, disaster prevention, fire tracking, monitoring areas, etc. Extending the WSN lifespan is significant for diverse applications, and the most effective model is optimizing Cluster Heads (CHs) through clustering. The dynamic CH formation and energy-aware clustering scheme help to improve the WSN lifetime. This study introduces an EnergyAware Dung Beetle Optimization-based Clustering Scheme (EADBO-CS) for WSNs. The EADBO-CS technique intends to achieve energy efficiency by grouping the nodes into clusters and electing CHs. The EADBO-CS technique mainly follows the behavior of Dung Beetle (DB) populaces. To achieve this, the EADBO-CS technique designs a Fitness Function (FF) comprising various parameters, namely Residual Energy (RE), Distance to Neighbors (DTN), Distance to Base Station (DBS), Node Degree (ND), and Node Centrality (NC). The design of multiple parameters helps in achieving improved energy efficiency and maximum longevity in the WSN. An investigational analysis is performed to validate the improved achievement of the EADBO-CS technique. The simulation values signify the supremacy of the EADBO-CS method over other present approaches with minimal Energy Consumption (ECON) of 0.150mJ and maximum Throughput (THRO) of 98.22Mbps.

Keywords:

Energy efficiency, Cluster head selection, Metaheuristics, Wireless sensor network, Dung beetle optimization.

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