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<p>Spectral activity detection is vital for Cognitive Radio Internet of Things (CR-IoT) networks, where weak primary user (PU) signals are often masked by fading and path loss. To address this, we propose an IRS-assisted cooperative spectrum sensing (CSS) framework that enhances signal reception and detection reliability in dense, low-SNR IoT environments. The proposed method leverages the programmable nature of RIS to dynamically reconfigure the wireless environment, thereby overcoming deep fading and improving sensing reliability. IRS integration with CSS enhances spectrum reuse and achieves reliable detection in dense, low-SNR IoT networks. Optimization of the IRS phase configuration is performed using the Fmincon algorithm, whereas the golden section method is employed to determine the optimal sensing duration. Simulation studies indicate notable improvements over conventional schemes in terms of throughput, detection probability, sensing time, and energy efficiency. This approach enables energy-efficient mobile IoT connectivity, supporting smart applications for public safety, rural access, and social impact.</p>

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Toward Green Cognitive IoT: IRS-Aided Cooperative Sensing Framework

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Jaspreet kaur,
  • Neelam Srivastava

摘要

Spectral activity detection is vital for Cognitive Radio Internet of Things (CR-IoT) networks, where weak primary user (PU) signals are often masked by fading and path loss. To address this, we propose an IRS-assisted cooperative spectrum sensing (CSS) framework that enhances signal reception and detection reliability in dense, low-SNR IoT environments. The proposed method leverages the programmable nature of RIS to dynamically reconfigure the wireless environment, thereby overcoming deep fading and improving sensing reliability. IRS integration with CSS enhances spectrum reuse and achieves reliable detection in dense, low-SNR IoT networks. Optimization of the IRS phase configuration is performed using the Fmincon algorithm, whereas the golden section method is employed to determine the optimal sensing duration. Simulation studies indicate notable improvements over conventional schemes in terms of throughput, detection probability, sensing time, and energy efficiency. This approach enables energy-efficient mobile IoT connectivity, supporting smart applications for public safety, rural access, and social impact.