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Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks

Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime co...

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Detalles Bibliográficos
Autores principales: Balaram, Allam, Babu, Rajendiran, Mahdal, Miroslav, Fathima, Dowlath, Panwar, Neeraj, Ramesh, Janjhyam Venkata Naga, Elangovan, Muniyandy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490580/
https://www.ncbi.nlm.nih.gov/pubmed/37687941
http://dx.doi.org/10.3390/s23177485
Descripción
Sumario:Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.