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A novel node selection scheme for energy-efficient cooperative spectrum sensing using D–S theory

  • Zilong Jin
  • Yu Qiao
Article
  • 44 Downloads

Abstract

Energy-efficient and reliable detection of available spectrum are fundamental objectives for cooperative spectrum sensing (CSS) in cognitive radio sensor networks (CRSNs). In this paper, a novel node selection scheme for energy-efficient CSS based on Dempster–Shafer (D–S) theory is proposed. Firstly, taking into account the historical data of nodes with historical reliability and residual energy of nodes, we propose a filtering strategy to filter out ineligible nodes, in order to reduce the computation loads in later steps and the amount of sensing results to be transmitted. Secondly, taking into account energy consumption balance of the network with the distance from the remaining nodes to fusion center, we propose a representative node selection algorithm for CSS, in order to reduce energy consumption. Thirdly, we consider that some representative nodes (R-Nodes) may not work as expected. Hence, facing this problem of malicious nodes in CRSNs, we propose an evaluation method based on D–S theory which considers simultaneously the current reliability and the mutually supportive degree among different R-Nodes to derive final decision. Simulation results show that the proposed scheme can not only reduce energy consumption, but can guarantee spectrum sensing accuracy, even in the presence of malicious nodes.

Keywords

Energy-efficiency Reliability evaluation Cooperative spectrum sensing Cognitive radio sensor networks D–S theory 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61602252 and 61702278), the Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20160967 and BK20160964), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 16KJB510024), and the China-USA Computer Science Research Center.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Department of Computer Science and EngineeringMichigan State UniversityLansingUSA

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