A systematic literature review on spectrum detection for Cognitive Radio-Internet of Things networks

The exponential growth of Internet of Things (IoT) devices has created a huge demand for Radio Frequency (RF) spectrum, only exacerbating the current congestion and shortage situation. This is where Cognitive Radio-Internet of Things (CR-IoT) systems come into play, which promises to bring in the functionality of dynamically managing spectrum resources to provide better spectral efficiency by recognizing and exploiting unused frequency bands, or spectrum holes. This systematic literature review investigates the integration of Cognitive Radio (CR) functionalities within IoT networks, focusing particularly on spectrum sensing techniques. Whereas conventional approaches rely on spectrum usage measurements in the frequency domain, temporal slots detection addresses a scarcely touched asset, i.e., predicting spectrum availability over time. Through an analysis of the literature, this paper brings out the advantages of integrating CR into IoT networks and reviews the prominent spectrum sensing techniques in CR-IoT scenarios. Moreover, by addressing the current research gaps in the review, especially from a temporal-aligned spectrum sensing perspective, we particularly highlight the gap of the limited attention to temporal spectrum sensing and suggests future directions for optimizing both frequency and temporal spectrum prediction to improve CR-IoT network performance.