Improvement of energy-efficient resources for cognitive internet of things using learning automata

The increasing demand for data collection from various Internet of Things (IoT) devices and limited energy of nodes in these networks led to the complex network conditions. Currently, the role of energy consumption for a large number of interconnected nodes in IoT is one of the research subjects. In many IoT applications, IoT devices are deployed in environments that are difficult to physically access. Therefore, an energy-efficient mechanism is important. This article proposes a new method for energy-efficient improvement based on machine learning from the point of view of cognitive networks, which is estimated using learning automata of network parameters. Then, transmission power of the network nodes to improve energy consumption is adjusted in a self-organized, self-aware and dynamic manner, and the behavior of the network nodes is adapted according to the current conditions of the network. One of the strengths of this method compared to the existing methods is that network conditions are estimated through parameters of Delay (D), Channel Status (S) and data rate, and this mechanism makes all decisions based on the network conditions. The results of the experiments show that examining energy-efficient from the perspective of cognitive network not only leads to the improvement of Quality of Service (QoS) parameters such as operational power and end to end delay in the network, but also an increase in the life of the network compared to other energy-efficient methods.