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.

Department of Basic Sciences College of Science and Theoretical Studies Saudi Electronic University Riyadh 11673 Saudi Arabia

Department of Biosciences Saveetha School of Engineering Saveetha Nagar Thandalam 602105 India

Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai 603203 India

Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur 522302 India

Department of Computer Science and Engineering MLR Institute of Technology Hyderabad 500043 India

Department of Control Systems and Instrumentation Faculty of Mechanical Engineering VSB Technical University of Ostrava 17 Listopadu 2172 15 70800 Ostrava Czech Republic

Department of R and D Bond Marine Consultancy London EC1V 2NX UK

School of Computing Graphic Era Hill University Dehradun 248002 India

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