A novel resource allocation method based on supermodular game in EH-CR-IoT networks

Abstract

Internet of Things (IoT) allows the connectivity of smart devices embedded with sensors, but with the growing problem of overcrowding in unlicensed bands, the data exchange in the network is severely disrupted. Besides, because Cognitive Radio IoT (CR-IoT) networks are composed of many small sensor devices, there is a great need for energy utilization efficiency. Building a new kind of Energy Harvesting enabled Cognitive Radio IoT (EH-CR-IoT) networks by applying EH technology and CR functions to IoT becomes an existing technical solution that can better solve problems such as scarce spectrum resources and valuable energy resources. In order to efficiently and reasonably manage resources such as energy and spectrum for EH-CR-IoT networks, Supermodular Game (SG) theory based resource allocation methods are proposed for both perfect spectrum sensing and imperfect spectrum sensing. The proposed methods first model the resource allocation problems in EH-CR-IoT networks as Bertrand game competition models, then design the utility functions of the consumers and the entire networks in terms of network pricing, next prove that the proposed Bertrand game competition models strictly comply with the theory of SG and the function solutions are Nonlinear Optimization Problems (NOP), after that the Nash Equilibrium (NE) solutions and the optimal network utility are obtained, finally simulation results are presented and prove the validity and the superiority of the proposed methods. Compared with conventional game methods, the proposed methods can better improve the network resource utilization and network benefits.