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The integration of cognitive radio network (CRNet) with internet of things (IoT) holds tremendous potential for creating more intelligent and advanced technological ecosystems. By allowing IoT devices to dynamically access and utilize the available radio spectrum, CRNet-based IoT systems can enable transparent connectivity, faster data transmission, and efficient resource utilization. The successful deployment of CRNet-based IoT systems highly depends on spectrum sensing implementation, which makes selecting the appropriate spectrum sensing technique extremely critical. However, several challenges are associated with the current spectrum sensing techniques, including limited representation of signal features, susceptibility to noise, inadequate hardware compatibility, and significant performance degradation using deep learning models. To deal with these issues, a novel approach is proposed integrating …