A Perusal Analysis on Hybrid Spectrum Handoff Schemes in Cognitive Radio Networks

  • J. Josephine DhivyaEmail author
  • M. Ramaswami
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


Spectrum handoff management is an important issue which needs to be addressed in Cognitive Radio Networks (CRN) for interminable connectivity and productive usage of unallocated spectrum for the unlicensed users. Spectrum handoff which comes under the phase of Spectrum mobility in CRN plays a vital role in ensuring seamless connectivity which is quite exigent. Handoff process in general comes under active and proactive types. The intelligent and hybrid handoff methods which combines both these types based on the network conditions proves to be quite satisfactory in the recent works. This paper proposes a hybrid novel method for handling the handoff mechanism based on Fuzzy rough set theory (FRST) with Support Vector machine (SVM), which enables the decision making stage of the handoff process more tenable and productive. The implied method predicts the node wherein handoff is to be initiated in the lead through which the handoff delay time and number of handoffs are minimized. The experimental results are compared with the previously proposed hybrid schemes including Fuzzy genetic algorithm (FGA) based handoff, FGA with cuckoo search (CS) optimization technique, FRS with CS and the findings portray the suggested methodology attains better prediction mechanism with minimal handoffs.


Spectrum handoff Fuzzy rough set Support Vector Machine 


  1. 1.
    Kumar, K., Prakash, A., Tripathi, R.: Spectrum handoff scheme with multiple attributes decision making for optimal network selection in cognitive radio networks. Digit. Commun. Netw. 3(4), 164–175 (2017)CrossRefGoogle Scholar
  2. 2.
    Bhatia, M., Kumar, K.: Network selection in cognitive radio enabled wireless body area networks. Digit. Commun. Netw. 1–11 (2018)Google Scholar
  3. 3.
    Ujarari, C.S., Kumar, A.: Handoff schemes and its performance analysis of priority within a particular channel in wireless systems. Int. J. Res. Appl. Sci. Eng. Technol. 3(5), 1021–1026 (2015)Google Scholar
  4. 4.
    Kumar, K., Prakash, A., Tripathi, R.: Spectrum handoff in cognitive radio networks: a classification and comprehensive survey. J. Netw. Comput. Appl. 61(C), 161–168 (2016)CrossRefGoogle Scholar
  5. 5.
    Christian, I., Moh, S., Chung, I., Lee, J.: Spectrum mobility in cognitive radio networks. IEEE Commun. Mag. 50(6), 114–121 (2012)CrossRefGoogle Scholar
  6. 6.
    Salgado, C., Hernandez, C., Molina, V.: Intelligent algorithm for spectrum mobility in cognitive wireless networks. Procedia Comput. Sci. 83, 278–283 (2016)CrossRefGoogle Scholar
  7. 7.
    Hernandez, C., Pedraza, E.: Multivariable adaptive handoff spectral model for cognitive radio networks. Contemp. Eng. Sci. 10(2), 39–72 (2017)CrossRefGoogle Scholar
  8. 8.
    Yan, S., Yan, X.: Vertical handoff decision algorithm based on predictive RSS and reduced fuzzy system using rough set theory. J. Inf. Comput. Sci. 12(12), 4677–4688 (2015)CrossRefGoogle Scholar
  9. 9.
    Mir, U., Munir, A.: An adaptive handoff strategy for cognitive radio networks. Wirel. Netw. 24(6), 2077–2092 (2017)CrossRefGoogle Scholar
  10. 10.
    Josephine Dhivya, J., Ramaswami, M.: Ingenious method for conducive handoff appliance in cognitive radio networks. Int. J. Electr. Comput. Eng. 8(2), 5195–5202 (2018)Google Scholar
  11. 11.
    Mardani, A., Nilashi, M., Antucheviciene, J., Tavana, M., Bausys, R., Ibrahim, O.: Recent fuzzy generalizations of rough sets theory: a systematic review and methodological critique of the literature. Complexity 2017, Article ID 1608147, 1–33 (2017)Google Scholar
  12. 12.
    Kumar, M., Yadav, N.: Fuzzy rough sets and its application in data mining field. Adv. Comput. Sci. Inf. Technol. 2(3), 237–240 (2015)Google Scholar
  13. 13.
    Cho, M.-Y., Hoan, T.T.: Feature selection and parameter optimization of SVM using particle swarm optimization for fault classification in power distribution systems. Comput. Intell. Neurosci. 1, 1–9 (2017)CrossRefGoogle Scholar
  14. 14.
    Eitrich, T., Lang, B.: Efficient optimization of support vector machine learning parameters for unbalanced data sets. J. Comput. Appl. Math. 196(2), 425–436 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Josephine Dhivya, J., Ramaswami, M.: A study on quantitative parameters of spectrum handoff in cognitive radio networks. Int. J. Wirel. Mob. Netw. 9(1), 31–38 (2017)CrossRefGoogle Scholar
  16. 16.
    Josephine Dhivya, J., Ramaswami, M.: Analysis of handoff parameters in cognitive radio networks on coadunation of wifi and wimax systems. In: IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials 2017, pp. 190–194. IEEE Xplore Digital Library, Chennai (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ApplicationsMadurai Kamaraj UniversityMaduraiIndia

Personalised recommendations