The Internet of Things (IoT) is a new model that contributes to connect heterogeneous objects and devices. It can share information with any device globally. Furthermore, the sensor technologies, communications, networks, and cloud computing technology in the IoT are expected to be integrated into large-scale monitored spaces. The main infrastructure of monitoring in the IoT is based on wireless sensor networks. Saving energy and expanding the lifetime are significant properties of the sensor’s nodes in these systems. The efficient energy saving projects should develop appropriate energy consumption’s algorithms to improve the lifetime of the networks in the IoT systems. In this paper, a routing algorithm is proposed to calculate the distributed cluster using cellular learning automata technology, which aims to reduce energy consumptions and can efficiently increase the network lifetime in the IoT system. The key objective of this project is reducing the data transmission distance from sensor nodes using the properties of the cluster structure. According to the cluster architecture, the data transmission distance between the sensors nodes can be reduced by balancing the network load, thereby reducing energy consumption and, consequently, enhancing the network lifetime. Simulation experiments revealed the superiority of this approach in the IoT to the previous ones with regard to energy consumptions and network lifetime.