Energy and Spectral Efficient Cognitive Radio Sensor Networks for Internet of Things

Abstract

Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios (CRs) are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for CR sensor networks (CRSNs). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient radio-frequency sources. We then propose an energy management scheme that consists of: 1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and 2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intracluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.