Cognitive radio (CR)-based Internet of Things (IoT) system is an effective step toward a world of smart technology. Many frameworks have been proposed to build CR-based IoT systems. The CR-based IoT frameworks are the key points on which this survey focuses. Efficient spectrum sensing and sharing are the main functional components of the CR-based IoT. Reviews of recent SS and sharing approaches are presented in this survey. This survey classifies the SS and sharing approaches and discusses the merits and limitations of those approaches. Moreover, this survey discusses the design factors of the CR-based IoT and the criteria by which the proper SS and access approaches are selected. Furthermore, the survey explores the integration of newly emerging technologies with the CR-based IoT systems. Finally, the survey highlights some emerging challenges and concludes with suggesting future research directions and open issues.

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Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which does not correspond to the reality of its usage, hence resulting in a partial waste of this resource. Considering jointly parameters like false alarm probability and detection probability, this article proposes a probabilistic model that can identify TVWS with improved accuracy. The proposed model considers energy detection criteria, combined with simultaneous sensing of the noise and of the signal from primary users. In order to demonstrate the model effectiveness, a low-cost Mobile Spectrum Sensing Station prototype was designed, implemented, and subsequently mounted on a vehicle. More than eight million spatio-temporally variant data samples were collected by scanning the UHF-TV spectrum of 500–698 MHz in the city of Windsor, ON, Canada. Analysis of the collected data showed that the proposed model achieves an accuracy improvement of about 9.6% compared to existing models, demonstrating that TVWS vary with spatial displacement and increasing significantly in the rural areas. Even in the most crowded spectrum zone, about 28% of the channels are identified as TVWS, and this number increases to a maximum of 60% in less crowded regions in urban areas. We conclude that the proposed model improves the TVWS detection compared with other used models, and also that the elements considered in this research contribute to reduce the complexity of the mathematical calculations while maintaining the accuracy. A low-cost open-source sensing station has been designed and tested, which represents an operative and useful data source in this setting.