A Channel Selection Strategy for Energy Harvesting in Cognitive Radio IoT Networks

Energy limitation and spectrum scarcity are becoming two critical issues in the design of Internet of Things networks. Two promising technologies, cognitive radio (CR) and radio frequency (RF) energy harvesting, can be jointly used to improve spectrum and energy efficiency. Thus, energy harvesting, and cognitive radio systems are becoming more inseparable for future IoT networks. This paper analyses the effect of selecting primary user (PU) channel by the secondary users on the performance of IoT networks metrics. Furthermore, we formulate an efficient channel selection strategy that is structured on multiarmed bandit (MAB) problem. The proposed channel selection scheme is based on a distributed channel selection strategy that combines reliable reputation model and multiarmed problem policies. With the proposed channel selection scheme, the SUs finds the best available PUs channels to maximize harvested RF energy. Simulation results validate the superiority of our proposed channel selection algorithm in terms of throughput and energy harvesting rate compared to Goodput based algorithms and ultra-reliability and low latency (URLL) based algorithms. that ensures that the SU's.