Energy Efficient Cluster Architecture and Spectrum Sensing for Cognitive Radio‐Based IoT Network

ABSTRACT In the Internet of Things (IoT), the modeling and development of energy‐efficient spectrum sensing is essential. With the existence of many smart devices, such as actuators, sensors, and different household gadgets, attaining such an efficient and scalable solution is challenging. The Cognitive Radio (CR) is an auspicious technology in IoT since it effectively resolves the spectrum scarcity issues. In the CR network, the efficient way to reduce energy consumption is the clustering process. Hence, this paper introduces the spectrum sensing technique to predict the spectrum availability based on the Taylor Namib Beetle Optimization algorithm‐based Deep Convolutional Neural network (TNBO_DeepCNN). Here, the TNBO is designed by altering the Namib Beetle Optimization (NBO) algorithm with the Taylor series. Initially, this research introduced the CR sensor Network (CRSN) using Fuzzy Local Information C‐Means Clustering (FLICM), and to improve the effectiveness of predicting spectrum availability, the signal features from the CRSN, such as signal energy, Eigen statistics, test statistics, wavelet, and matched filter, are extracted. Moreover, the spectrum availability is predicted by the Deep CNN by considering all the signal features. The significance of this work lies in the integration of a biologically inspired optimization algorithm with deep learning for enhanced spectrum prediction, offering a scalable and adaptive solution for next‐generation IoT networks. Experimental results validate the impact of the proposed TNBO_DeepCNN in terms of reduced computation time and energy consumption, as well as improved spectrum detection performance, demonstrating its potential for real‐time and energy‐sensitive IoT applications. The experimental results assures that the TNBO_DeepCNN achieved better computation time, network energy, probability of false alarm, probability of detection, sensing time, and probability of miss detection of 0.468 s, 0.058 J, 0.007, 1.000, 187.42 ms, and 0.0002, respectively. The high‐performance results make the model practically applied in smart city infrastructure, autonomous vehicles, and industrial IoT systems, where real‐time, energy‐efficient, and reliable spectrum access is critical.