Efficient Resource Allocation for Real Time Traffic in Cognitive Radio Internet of Things

Recent development and research in the field of communication technologies have congested the unlicensed spectrum. It has resulted in uncontrolled and unrestricted interference to the low-powered wireless sensor network based Internet of Things (IoT). On the other hand, these advancements have necessitated the low-powered IoT to be designed with limited cost, low-energy consumption and efficient spectrum utilization. The issue of spectrum utilization is solved by cognitive radio (CR) network, a low-cost solution to utilize the spectrum efficiently. In CR networks the underutilized licensed spectrum is exploited by unlicensed users opportunistically. Due to their opportunistic nature, the performance of these networks depends on the observed spectrum pattern of a primary user. Therefore, perfect modeling of the spectrum detection and utilization is required in these networks. In this paper, we propose a primary user detection model for Cognitive Radio based Internet of Things (CR-IoT) using the hidden Markov model. We introduced two algorithms; one for free channel detection using the concept of HMM, and the second for efficient allocation of free detected channels. The simulation results show that CR-IoT outperformed traditional networking schemes.