Optimization of Sensing Time in Cognitive Radio Networks Based on Localization Algorithm

  • P. PoornimaEmail author
  • S. Chithra
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


Cognitive radio is one of the promising technologies that allows opportunistic spectrum access for unlicensed users namely the secondary users (SUs) to utilize the white spaces available in the spectrum allocated to the primary user (PU). To achieve a power efficient spectrum sensing the geometry-based localization algorithm is proposed. Here, the deployment of primary and secondary users are first considered. Then the secondary users are categorized based on their known and unknown locations. In order to locate the unknown secondary user, within the prescribed geometry, is identified by employing the Spider Monkey Optimization algorithm. To ensure optimal convergence for the given problem statement N number of iterations are done and best values are calculated using the fitness factor of the samples. Based on the best values, the optimized location of the unknown SUs is recorded and priority is given to those SUs that remain close to the PU and maintains a proper degree of separation. Since wireless devices are more vulnerable to security threats, this paper also aims in identifying malicious nodes and prohibiting them to access the white spaces by inducing denial of service through primary user to safeguard the spectrum access by outliers. Simulation results also provide a satisfactory outcome for achieving power effective spectrum sensing and performance of the proposed algorithm is studied in the presence of malicious and normal nodes.


Spectrum sensing Spider monkey optimization White spaces Malicious node detection 


  1. 1.
    Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999). Scholar
  2. 2. Federal Communications Commission Office Of Engineering and Technology Policy and Rules Division
  3. 3.
  4. 4.
    Fragkiadakis, A.G., Tragos, E.Z., Askoxylakis, I.G.: A survey on security threats and detection techniques in cognitive radio networks. In: IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 428–445 (2013). Scholar
  5. 5.
    Saeed, N., Nam, H.: Energy efficient localization algorithm with improved accuracy in cognitive radio networks. IEEE Commun. Lett. 21(9), 2017–2020 (2017). Scholar
  6. 6.
    Guibène, W., Slock, D.: Cooperative spectrum sensing and localization in cognitive radio systems using compressed sensing. J. Sens. 2013, 9 (2013). Article ID 606413CrossRefGoogle Scholar
  7. 7.
    Farid, Z., Nordin, R., Ismail, M.: Recent advances in wireless indoor localization techniques and system. J. Comput. Netw. Commun. 2013, 12 (2013). Article ID 185138CrossRefGoogle Scholar
  8. 8.
    Yongcai, A., Bo, Z., Baozhuo, Z., Shili, W.: Change of geometric dilution of precision (GDOP) for integrated system. In: 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, pp. 660–662 (2016).
  9. 9.
    Bansal, J.C., Sharma, H., Jadon, S.S.: Memetic Comp. 6, 31 (2014). Scholar
  10. 10.
    Al-Azza, A.A., Al-Jodah, A.A., Harackiewicz, F.J.: Spider monkey optimization: a novel technique for antenna optimization. IEEE Antennas Wirel. Propag. Lett. 15, 1016–1019 (2016). Scholar
  11. 11.
    Clancy, T.C., Goergen, N.: Security in cognitive radio networks: threats and mitigation. In: 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), Singapore, pp. 1–8 (2008).
  12. 12.
    Chen, R., Park, J., Reed, J.H.: Defense against primary user emulation attacks in cognitive radio networks. IEEE J. Sel. Areas Commun. 26(1), 25–37 (2008). Scholar
  13. 13.
    Sharp, I., Yu, K., Guo, Y.J.: GDOP analysis for positioning system design. IEEE Trans. Veh. Technol. 58(7), 3371–3382 (2009). Scholar
  14. 14.
    Yu, Y.H., Sun, C., Qin, N.N., Gao, K., Chen, D.Z.: CR-RSS location algorithm for primary user in cognitive radio. J. China Univ. Posts Telecommun. 21(1), 22–25 (2014). Scholar
  15. 15.
    Liu, X., Li, F., Na, Z.: Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio. IEEE Access 5, 3801–3812 (2017). Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Annai College of Engineering and TechnologyKumbakonamIndia
  2. 2.SSN College of EngineeringChennaiIndia

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