The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols.

能量收集方法使WSN节点可以在能量收集子系统的帮助下永久永久地保存能量，并持续提供能量并将其存储以备将来使用。能量收集技术可以使用各种潜在的能源，例如太阳能，风能，机械能和温度变化。受能量限制的传感器节点尺寸很小。因此，需要一些机制来减少能耗，从而延长网络寿命。聚类机制用于WSN中的能源效率。在集群机制中，传感器节点组形成集群。聚类过程的性能取决于各种因素，例如最佳的聚类形成数量和聚类头选择过程。在本文中，我们提出了一种基于混合鲸鱼和灰太狼优化（WGWO）的聚类机制，用于能量收集无线传感器网络（EH-WSNs）。在提出的研究中，我们使用鲸鱼和灰太狼这两种元启发式算法来提高聚类机制的有效性。在算法评估期间，所提出的混合WGWO方法的开发和探索能力远高于传统的各种现有的元启发式算法。这种混合方法可提供最佳结果。提出的基于混合鲸灰狼优化的聚类机制由聚类形成和动态聚类头（CH）选择组成。将所提出的方案的性能与现有的最新路由协议进行比较。