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Research Article |

Spectrum Sensing of Cognitive Radio – A Survey

Author(s): R. Joash Paul Timothy* and J. Christopher Clement

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 4, Issue 1

Publisher : FOREX Publication

Published : 30 march 2016

e-ISSN : 2347-470X

Page(s) : 20-29

DOI: https://doi.org/10.37391/IJEER.040105

Abstract

Cognitive radio is emerging as one of the most promising aspects regarding the efficient usage of the radio spectrum and also on a non-interference basis. However the most challenging part is the effective detection of primary users (PUs). Nowadays there are a lot of threats from attackers who use techniques like data falsification, primary user emulations to cause harm to the users, so we need to address them with proper and efficient solutions. So in this survey we address the various threats and the challenges faced in cognitive radio environments and also we are here to discuss the various sampling techniques that could be used for the purpose of proper detection.

Keywords: Cognitive Radio, Dynamic Spectrum, Primary User Emulation.




R. Joash Paul Timothy*, Department of Communication Engineering School of Electronics Engineering, Vellore Institute of Technology,Vellore – 632014, Tamilnadu, India; Email: rjoashpaul@gmail.com

J. Christopher Clement , Department of Communication Engineering School of Electronics Engineering, Vellore Institute of Technology,Vellore – 632014, Tamilnadu, India; Email: christopher.clement@vit.ac.in

    [1] H Celebi, H Arslan, Utilization of location information in cognitive wireless networks, IEEE Wireless Communication. 14(4), 6–13 (2007).
    [2] J Chen, L Jiao, J Wu, X Wang, Compressive spectrum sensing in the Cognitive Radio Networks by exploiting the scarcity of active radios. WirelessNet 19(5), 661– 671 (2013).
    [3] C Chen, M Song, CS Xin, CoPD: A Conjugate prior based Detection Schemeto countermeasure Spectrum Sensing data Falsification Attacks in Cognitive Radio Networks, Wireless Net. 20(8), 2521–2528 (2014).
    [4] AW Min, KG Shin, X Hu, Attack-tolerant distributed sensing for dynamic Spectrum access networks. 17th IEEE International Conference on Network Protocols, ICNP 2009, 2009, p. 294.
    [5] Y Zhang, N Meratnia, P Havinga, Outlier detection techniques for wireless sensor networks: A survey, IEEE Communications Surveys Tutorials 12(2), 159– 170 (2010).
    [6] FastProbe: Malicious User Detection in Cognitive Radio Networks through Active Transmission Tarun Bansal, Bo Chen and Prasun Sinha.
    [7] A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks Alexandros G. Fragkiadakis, Elias Z. Tragos, Ioannis G. Askoxylakis.
    [8] A Survey of Security Challenges in Cognitive Radio Networks: Solutions and Future Research Directions By Alireza Attar, Helen Tang, Athanasios V. Vasilakos, Senior Member IEEE, F. Richard Yu, Senior Member IEEE, and Victor C. M. Leung, Fellow IEEE.
    [9] A survey on security attacks and countermeasures with primary user detection in cognitive radio networks by José Marinho, Jorge Granjal, and Edmundo Monteiro.
    [10] Reputation-Based Cooperative Spectrum Sensing with Trusted Nodes Assistance by Kun Zeng, Przemysław Pawełczak, and Danijela Cabric.
    [11] Toward Secure Distributed Spectrum Sensing in Cognitive Radio Networks: Ruiliang Chen, Jung-Min Park, Y. Thomas Hou, and Jeffrey H. Reed, Virginia.
    [12] P.K. Varshney, Distributed detection and data fusion springer-Verlag.
    [13] Outlier Detection Techniques for Wireless Sensor Networks: A Survey Yang Zhang, Nirvana Meratnia, and Paul Havinga.
    [14] Trust-based Data Fusion Mechanism Design in Cognitive Radio Networks by Ji Wang, Ing-Ray Chen.
    [15] Y. Cai, L. Cui, K. Pelechrinis, P. Krishnamurthy, M. B. Weiss, and Y. Mo,“Decoupling trust and Wireless channel induced effects on collaborative Sensing attacks,” in 2014 IEEE International Symposium on DynamicSpectrum Access Networks (DYSPAN), 2014.
    [16] A Mitigation Strategy against Malicious Primary User Emulation Attack in Cognitive Radio Networks: Bilal Naqvi, Shafaq Murtaza, Baber Aslam.
    [17] Defense against Primary User Emulation Attacks in Cognitive Radio Networks: Ruiliang Chen, Jung-Min Park, and Jeffrey H. Reed.
    [18] Cognitive Radio: Making Software Radios More Personal Joseph Mitola and Gerald. q. Maguire.
    [19] Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon’s Outlier Detection Method: Sanket S. Kalamkar and Adrish Banerjee and Ananya Roychowdhury.

R. Joash Paul Timothy and J. Christopher Clement (2016), Spectrum Sensing of Cognitive Radio – A Survey. IJEER 4(1), 20-29. DOI: 10.37391/IJEER.040105.