Wireless sensor networks (WSNs) consist of a large number of sensor nodes, which are primarily employed for collecting data from an environment of interest. Energy resources of WSN nodes are generally restricted, irreplaceable and non-rechargeable. Hence, lowering the level of energy consumption in such networks to save more energy is the key issue in the literature. Clustering, selecting the best Cluster Head (CH) among candidates, and performing the routing only among cluster heads would be an effective approach to reduce the WSN nodes energy consumption. Therefore, cluster-based routing leads to extending the network’s lifetime through aggregating data in CHs, uniformly distributing the energy among nodes, and, consequently, reducing the number of contributing nodes in the routing procedure. In this paper, an energy-aware cluster-based multi-hop routing algorithm is presented, in which the clusters would, if required, re-formed during the routing procedure. Furthermore, like other multi-hop routing algorithms, it guarantees minimizing the energy consumption through balancing energy within the network. In this paper, we have presented a cluster-based multi-hop routing algorithm. In our proposed approach, a combination of two algorithms, namely K-means and Open Source Development Model Algorithm (ODMA), are employed for clustering, and Genetic Algorithm, is applied for multi-hop routing. The simulation results confirm superiority of our proposed method in comparison with MH-FCM, EEWC, and GAFOR algorithms in terms of several metrics such as average residual energy, residual energy variance, number of packets received, number of dead nodes, and network lifetime.

无线传感器网络 (WSN) 由大量传感器节点组成，主要用于从感兴趣的环境中收集数据。WSN节点的能源资源一般是有限的、不可替代的和不可充电的。因此，降低此类网络中的能源消耗水平以节省更多能源是文献中的关键问题。聚类、在候选中选择最佳簇头（CH）并仅在簇头之间执行路由将是降低 WSN 节点能耗的有效方法。因此，基于集群的路由通过聚合 CH 中的数据、在节点之间均匀分配能量来延长网络的生命周期，从而减少路由过程中贡献节点的数量。在本文中，提出了一种基于能量感知集群的多跳路由算法，如果需要，集群将在路由过程中重新形成。此外，与其他多跳路由算法一样，它通过平衡网络内的能量来保证最小化能量消耗。在本文中，我们提出了一种基于簇的多跳路由算法。在我们提出的方法中，两种算法的组合，即 K-means 和开源开发模型算法 (ODMA)，用于聚类，遗传算法用于多跳路由。仿真结果证实了我们提出的方法与 MH-FCM、EEWC 和 GAFOR 算法相比在平均剩余能量、剩余能量方差、接收到的数据包数量、死节点数量、