Edge computing for Internet of Things (IoT) is a promising framework that can help small devices such as low-powered sensor nodes to accomplish complex computational tasks. The limited power supply of sensor nodes is one of their major limitations. A successful approach to improve the network lifetime and the overall scalability of the IoT supported wireless sensor networks (WSNs) is clustering. However, in a clustered IoT supported WSNs, some of the Cluster Heads (CHs) bear more traffic load than the others and therefore die sooner leading to decrease the network lifetime. To overcome this problem and maximize the network lifetime, the load of the CHs must be balanced. This research work suggests a new clustering method to balance the traffic load imposed on the cluster heads in IoT supported WSNs. The proposed clustering method uses a 1.2-approximation algorithm. In addition, we introduce an energy-aware routing algorithm for transmitting data packets from the CHs to their destination. The proposed routing algorithm distributes the communication load of the data packets among more nodes near the destination by a proper segmentation of the area. The simulation results show that the proposed clustering and routing algorithms in addition to being practical for large-scale IoT supported WSNs, cause the network to have a better performance compared to other similar algorithms.

物联网 (IoT) 边缘计算是一种很有前景的框架，可以帮助低功耗传感器节点等小型设备完成复杂的计算任务。传感器节点的有限电源是它们的主要限制之一。提高物联网支持的无线传感器网络 (WSN) 的网络寿命和整体可扩展性的一种成功方法是集群。然而，在支持集群物联网的 WSN 中，一些簇头 (CH) 比其他簇头 (CH) 承受更多的流量负载，因此会更快死亡，从而导致网络寿命缩短。为了克服这个问题并最大限度地延长网络寿命，必须平衡 CH 的负载。这项研究工作提出了一种新的聚类方法来平衡施加在物联网支持的 WSN 中簇头的流量负载。建议的聚类方法使用 1。2-近似算法。此外，我们引入了一种能量感知路由算法，用于将数据包从 CH 传输到其目的地。所提出的路由算法通过对区域进行适当的分割，在目的地附近的更多节点之间分配数据包的通信负载。仿真结果表明，所提出的聚类和路由算法除了适用于支持大规模物联网的 WSN 之外，还使网络与其他类似算法相比具有更好的性能。