## 1 Introduction
### Motivations for Using SD-CRNs in IoT  
SD-CRN is a state-of-the-art communication paradigm, and is one of the potential technologies adopted for the IoT and other futuristic M2M applications [2; 3; 4; 5; 6; 7]. SD-CRN exploits the dynamic spectrum allocation (DSA) and spectrum diversity to address the spectrum scarcity and under-utilization issues. Enabling cognitive radio (CR) features in IoT (CR-IoT) helps alleviate congestion in the network; hence, increases the utilization efficiency of spectrum, and helps to meet the spectrum demands of the future IoT. The proposed model is based on the case-study scenarios where IoT devices (with CR features) coexist (share a common frequency band) with any licensed primary cellular network, and can dynamically access the spectrum assigned to a licensed primary network [8; 9]. It is not spectral- and cost-efficient for network service providers to utilize an additional licensed spectrum only for IoT services. Therefore, spectrum sharing based on CR features is needed for IoT networks. For (resource/power) constrained IoT devices, simple signal processing techniques are needed to save energy, and reduce system cost. Hence, the simplest non-coherent energy detection (as sensing technique) is preferred as in the previous research [10; 11]. In our proposed system, we also consider energy based sensing. Moreover, if IoT devices are restricted to perform the sensing procedure (due to resource/power constraints), the sensing procedure can be employed only for the fusion center (FC) [12]. The SUs in CR-IoT exploit the spectrum under the interference constraint and ensure uninterrupted M2M or human-based LTE communications. The CR features also provide a cognitive facility to the IoT devices that are required to make smart decisions regarding the spectrum and to perform intelligent operations by analyzing the network conditions. The multi-tiered hierarchy in CRNs categorizes users into two types: primary users (PUs) and secondary users (SUs). The DSA techniques enable time division multiple access (TDMA)-based spectrum sharing between the PUs and SUs. However, the spectral efficiency (or spectrum utilization) of CRNs depends significantly on the successful integration of the PUs and SUs. It requires that the SUs be capable of sensing and keeping track of primary transmissions precisely. The three main types of DSA-based approaches are overlay, underlay, and interweave [13; 14; 15]. In an underlay approach, the SU and PU can transmit simultaneously but with a constraint for the SU to reduce its transmission power when the PU is active. In overlay, an SU transmits simultaneously with a PU, and assists the primary transmissions with certain relaying techniques. In interweave (opportunistic spectrum access (OSA)-based approach), an SU exploits the spectral opportunities and can transmit only when an idle spectral slot is sensed to be idle. In Interweave-based CR systems, the SUs exploit the spectral opportunities through the spectrum sensing. Spectrum sensing is performed either independently or in a cooperative manner. Perfect spectrum sensing is impossible to achieve in the realistic wireless scenarios. Thus, the sensing errors due to imperfect sensing must be considered for the precise analysis of the CR-IoT. The sensing performance can be enhanced by minimizing the sensing errors. Further, the sum rate of the network, as well as the utilization for the PU and SU nodes, can be improved by choosing the optimal sensing operating points under the imperfect sensing scenarios [5; 6; 7; 15; 16]. In summary, integrating optimal CR technology into IoT can alleviate the spectrum scarcity issues and contribute in the process of IoT developments.