Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making

Abstract

The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.