# Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks
## 3 A Brief Review of Articles in Special Issue  
The massive deployment of IoT devices requires additional spectrum resources to provide guaranteed QoS of future wireless networks. However, a major obstacle to resolve this problem is spectrum depletion. Many techniques are employed to reuse or share the spectrum among multiple users without degrading the system performance. [PERSON] et al. [7] proposed an enhanced spectrum sharing technique by utilizing kullback leibler divergence (KLD). Their approach shows a reasonable sensing performance even with the limited sample for sensing and noise uncertainty. They provided mathematical analysis and experiments to show the effectiveness of the proposed scheme. It is shown that the proposed sensing method can achieve the improved probability of detection and higher sum rate compared to the conventional techniques. Satellite communication networks provide extensive comprehensive coverage to the remotely deployed devices. A digital channelizer is used to provide support for multiple transponders in satellite communications. [PERSON] et al. [8] proposed an energy monitoring dependent cognitive communication system for detecting and cancelling interference. They used the inherent properties of the digital channelizer and fast fourier transform (FFT) to improve interference detection performance. They evaluated the analytical sensing performance mathematically, and used the empirical results to verify it. The results showed that the proposed method tackled tone interference detection and cancellation effectively. Technology innovation to make devices energy efficient is accelerating at an unprecedented pace. It is also ideal for wireless devices with limited power to increase the device lifetime and, consequently, network lifetime [9]. In case of network bottleneck, wireless sensor networks (WSN) can use CR technology to increase throughput. The leasing of PU's spectrum to SU is one of the viable options to increase the spectrum reusability and profit for service providers. [PERSON] et al. [10] proposed a differential game model for energy efficient allocation of cognitive WSN resources. By using open loop Nash equilibrium and feedback Nash equilibrium, the authors formulated optimal game control strategies for SU. They evaluated the proposed game technique with numerical simulations. The results show that the optimal solutions for cognitive WSN users are available to ensure the accuracy and efficacy of the proposed scheme. In the literature, the relaying wireless sensor nodes are used to efficiently transmit the data to the destination. It increases the transmission capacity of the network, and saves energy. The network consists of secondary source (SS) node, secondary relay (SR) nodes, secondary destination (SD) nodes, and primary destination (PD) nodes. [PERSON] et al. [11] proposed a cooperative phase steering (CPS) technique with duty cycling to maximize spectrum sharing and boost energy efficiency. They evaluated the CPS mathematically first, and then tested the scheme using comprehensive mathematical simulations. The results show the improved efficiency as compared with the traditional relay selection scheme in terms of throughput and outage probability. The application of unmanned aerial vehicles (UAV) is growing in smart cities and IoT. The concern about reliability and security is especially essential for ensuring protection and preventing any accidents. Energy harvested UAV communications are essential to prolonging battery life. CR based communication for UAV may be useful by utilizing already available spectrum without dedicated channel and increase coverage. [PERSON] et al. [12] proposed UAV energy management scheme based on CR technology. They developed an analytical model for the connection outage probability, secrecy outage probability, and residual energy. To minimize the energy consumption, they devised optimal sensing duration and transmission power.