Energy Efficient Cooperative Spectrum Sensing with Energy Harvesting for Optimal Node Selection and Channel Access in Cognitive IoT Networks

Abstract

The exponential growth of smart gadgets connected to the Internet as well as diverse applications has escalated the spectrum scarcity problem. Cognitive radio based spectrum sensing technique becomes a potential approach for detecting idle spectrum in licensed channels and gaining access to it on an as-needed basis. This improves the spectral efficiency in cognitive IoT networks. However, to support the cognitive radio features, the IoT sensor nodes demand a large amount of energy. In this research, we present a technique for improving detection performance and energy economy in cognitive IoT systems by combining spectrum sensing and energy harvesting. The IoT nodes are expected to be capable of spectrum detection and energy harvesting. We use a game theoretic technique to pick relevant IoT nodes for cooperative spectrum sensing based on their energy restrictions. Furthermore, we formulate an optimal channel assignment mechanism to improve opportunistic spectrum utilization. We develop a branch and bound based heuristic approach with low computing complexity to address the optimization problem. Various system parameters are used to evaluate the proposed system’s performance. When compared to other current models, simulation results show that the proposed approach greatly improves energy efficiency and detection performance.