Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic Whale Optimization Algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks Optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

物联网 (IoT) 中的智能对象相互通信、收集信息并响应用户请求。在这些系统中，无线传感器用于收集数据并监控最低级别的环境。在物联网应用中，无线传感器网络发挥着关键作用。由于物联网设备经常使用电池，因此效率对它们来说很重要，因此物联网相关标准和研究工作更多地关注节能或保护。在本文中，我们使用了两种元启发式算法来进行物联网中的集群和路由。我们使用基于元启发式鲸鱼优化算法（WOA）的称为 WOA 聚类的聚类方法对网络进行聚类，并选择最佳簇头。然后，我们使用基于 Harris Hawks Optimization (HHO) 算法（一种新颖的元启发式算法）的称为 HHO-Routing 的路由方法，将簇头路由到 BS。上述方法的使用导致到达基站(BS)的功耗降低。此外，为了证明所提出方法的最佳性能，对这些方法进行了模拟，并在相似的背景下与五种不同的方法进行了比较。观察到消耗的能量、第一个节点死亡的生存周期数以及数据传输速率都有所提高。所提出的方法在cooja模拟器中进行了模拟，为了更准确的评估，我们将其与UCCGRA、PSO-SD、PUDCRP、EECRA、EEMRP算法进行比较。我们发现所提出的方法在能耗和网络寿命方面比其他方法表现更好。