What question did this study set out to answer?

The aim is to develop an energy-efficient spectrum sensing method for cognitive radio in IoT networks.

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

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Randomized trial demonstrates improved spectrum detection in IoT networks, highlighting energy efficiency implications.

The aim is to develop an energy-efficient spectrum sensing method for cognitive radio in IoT networks.
Introduced the Cognitive Radio Sensor Network (CRSN) using Fuzzy Local Information C-Means Clustering (FLICM).
Implemented the Taylor Namib Beetle Optimization algorithm-based Deep Convolutional Neural network (TNBO_DeepCNN) for spectrum prediction.
Extracted signal features like signal energy, Eigen statistics, and wavelet for enhanced spectrum availability prediction.
Achieved reduced computation time of 0.468 seconds and energy consumption of 0.058 Joules.
Probability of false alarm was 0.007 and probability of detection was 1.000, demonstrating high accuracy.
Improved sensing time of 187.42 ms and a probability of miss detection of 0.0002, indicating effective performance.

Jaronde et al. (2026) studied this question.

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