Balaram Allam, Babu Rajendiran, Mahdal Miroslav, Fathima Dowlath, Panwar Neeraj, Ramesh Janjhyam Venkata Naga, Elangovan Muniyandy
Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad 500043, India.
Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Chennai 603203, India.
Sensors (Basel). 2023 Aug 28;23(17):7485. doi: 10.3390/s23177485.
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.
无线传感器网络(WSNs)实现了传感器节点之间的通信,并且在各种条件下都需要高效的能量管理以实现最佳运行。关键挑战包括最大化网络寿命、覆盖区域以及有效的数据聚合与规划。更长的网络寿命有助于提高数据传输的耐久性、节省传感器资源以及实现可扩展性。本文介绍了一种用于资源高效的无线传感器网络的增强型双选磷虾群(KH)优化聚类方案,该方案具有最小的开销。所提出的方法提高了整体能量利用率,减少了节点间通信,将无线传感器网络在节点部署和聚类中的节能挑战作为优化问题来解决。采用动态分层机制来防止重复选择相同的簇头节点,确保有效的双选。我们的算法旨在通过增强的开发和探索过程,利用改进的基于磷虾的聚类方法来识别最优解。与基准方法的比较分析表明,所提出的模型将网络寿命提高了23.21%,增加了稳定能量19.84%,并减少了网络延迟22.88%,为无线传感器网络的能量管理提供了一种更高效、可靠的解决方案。