Energy-Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network

• Published in: IEEE transactions on vehicular technology Vol. 66; no. 1; pp. 831 - 843
• Main Authors: Deyu Zhang, Zhigang Chen, Ju Ren, Ning Zhang, Awad, Mohamad Khattar, Haibo Zhou, Xuemin Sherman Shen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognitive radio; Cognitive radio (CR); Data communication; Energy consumption; energy efficiency; Energy harvesting; energy harvesting (EH); multiple channels; Resource management; Sensors; wireless sensor network (WSN); Wireless sensor networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2016.2551721

The incorporation of cognitive radio (CR) and energy harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy-efficient heterogeneous CR sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and battery-powered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, whereas data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this paper, we propose a resource-allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve the energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem: a spectrum sensor scheduling (SSS) algorithm and a data sensor resource allocation (DSRA) algorithm. The SSS algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and primary user (PU) transmissions are protected. The DSRA algorithm allocates the transmission time, power, and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced, while maintaining the sustainability of the spectrum sensors.