A GAME THEORETIC COGNITIVE SPECTRUM SENSING SCHEME FOR IoT NETWORKS

Abstract

The game theory-based cognitive spectrum sensing model proposed in this paper is an innovative approach to optimizing spectrum consumption in Internet of Things (IoT) networks, combining cognitive radio techniques with game-theoretic methodologies. It enables IoT devices to intelligently identify and utilize unused frequency bands, ensuring efficient communication in the face of growing wireless resource demands. Each IoT device is viewed as a participant in a game, aiming to optimize spectrum usage while minimizing interference with primary users and other devices. Adaptive learning algorithms, such as Q-learning, are used to improve spectrum access techniques, resulting in a highly responsive and flexible network. The model prioritizes energy economy, aiming to reduce operational costs and extend battery-powered IoT device longevity. It effectively manages trade-offs between energy consumption, spectrum sensing accuracy, and communication quality, making it suitable for various IoT applications. The model's collaborative and competitive elements ensure just and impartial spectrum allocation, making it a scalable and sustainable approach for managing the spectrum in IoT networks. The network offers high throughput, which is 200% more compared to the existing methods, and offers the fastest services enabling robust application development in resource-limited IoT networks.