With the increasing expansion of the Internet of Things (IoT) in applications such as environmental monitoring, agriculture, and smart cities, wireless sensor networks (WSNs) have become the main data collection tools. Energy constraints and spectrum interference are important challenges for these networks. Combining cognitive radio (CR) with WSNs has led to the formation of cognitive wireless sensor networks (CR-WSNs) that have higher spectral efficiency, but due to spectrum sensing and channel selection, they face higher energy consumption and latency. Also, the large volume of sensor data increases traffic and reduces network lifetime. To solve these problems, the present study introduces a composite index called RCEI that combines criteria such as packet delivery rate, residual energy, link stability, spectrum availability, and latency in a weighted manner. The main innovation is the integration of this index with data compression based on polynomial series. Simulation results show that the proposed method increases the network lifetime, reduces energy consumption, and improves communication stability compared to reference methods.
The aim of this combination is to increase the network lifetime, improve communication stability, and optimize energy consumption in CR-WSN. Simulation results show that the proposed method achieves significant improvements in network lifetime, reduces energy consumption, increases packet delivery rate, and reduces end-to-end delay compared to reference methods.