Biologically-Inspired Foraging Decision Making in Distributed Cognitive Radio Networks

  • Olukayode A. Oki
  • Thomas O. Olwal
  • Pragasen Mudali
  • Matthew Adigun
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 683)

Abstract

The dynamic spectrum management techniques have been introduced to address the current Radio Frequency bands inefficiency challenges. Cognitive Radio (CR) technology has been regarded as the most promising technology in the dynamic spectrum management area. One of the major aspects of the spectrum management is the decision making ability of CR users. The dynamic reconfiguration of both the operating frequency and channel bandwidth in a distributed CR network has not received sufficient attention despite their importance in spectrum decision making. Few research works have attempted to address the dynamic reconfiguration of frequency and channel bandwidth problems using various approaches. However, due to certain challenges such as high computational complexity, ambiguity, repeatability and the lack of optimality with the existing approaches, researchers are still trying to explore newer methods that can achieve optimal spectrum management. Hence, this paper presents a biologically-inspired optimal foraging model for dynamic reconfiguration of frequency and channel bandwidth in a distributed cognitive mobile adhoc network. One of the main advantages of biologically-inspired foraging model is its analytical simplicity and optimum solution. The mean efficiency and Distance travelled by SUs before finding available frequency were measured. The two metrics were measured when subjected to different SUs positions and Giving-Up Time. It was generally observed that the SUs perform better when 0 < Xo ≤ 0.2 and GUT ≤ 50 in the achieved mean efficiency and distance travelled to find available frequency.

Keywords

Cognitive radio Distributed Decision making Foraging Spectrum 
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References

  1. 1.
    Masonta, M.T., Mzyece, M., Ntlatlapa, N.: Spectrum decision in cognitive radio networks: a survey. IEEE Commun. Surv. Tutor. 15(3), 1088–1107 (2013)CrossRefGoogle Scholar
  2. 2.
    Mitola, J.: Cognitive radio – model-based competence for software radios. Licentiate thesis, KTH, Stockholm, September 1999.Google Scholar
  3. 3.
    Farzad, H., Sumit, R.: Capacity considerations for secondary networks in TV white space. IEEE Trans. Mobile Comput. 1–29 (2013). arXiv: 1304. 1785v1Google Scholar
  4. 4.
    Marinho, J., Monteiro, E.: Cognitive radio: survey on communication protocols, spectrum decision issues and future research directions. J. Wirel. Netw. 18(2), 147–164 (2012)CrossRefGoogle Scholar
  5. 5.
    Dere, B.A., Bhujade, S.: An efficient spectrum decision making framework for cognitive radio networks. Int. J. Innov. Sci. Modern Eng. (IJISME) 3(2), 45–48 (2015)Google Scholar
  6. 6.
    Akyildiz, I.F., Won-Yeol, L., Vuran, M.C., Mohanty, S.: A survey on spectrum management in cognitive radio networks. IEEE Commun. Mag. 2(3), 40–48 (2008)CrossRefGoogle Scholar
  7. 7.
    Sengupta, S., Subbalakshmi, K.P.: Open research issues in multi-hop cognitive radio networks. IEEE Commun. Mag. 2(3), 168–176 (2013)CrossRefGoogle Scholar
  8. 8.
    Oki, O.A., Olwal, T.O., Mudali, P., Adigun, M.O.: Dynamic spectrum reconfiguration for distributed cognitive radio networks. J. Intell. Fuzzy Syst. 32(4), 3103–3110 (2017)CrossRefGoogle Scholar
  9. 9.
    Atakan, B., Akan, O.B.: Biological foraging-inspired communication in intermittently connected mobile cognitive radio ad hoc networks. IEEE Trans. Veh. Technol. 61(6), 2651–2658 (2013)CrossRefGoogle Scholar
  10. 10.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization. IEEE Control Syst. Mag. 22(3), 52–67 (2002)CrossRefGoogle Scholar
  11. 11.
    Quijano, N., Passino, K.M., Andrews, B.W.: Foraging theory for multi-zone temperature control. IEEE Comput. Intell. Mag. 1(4), 18–27 (2006)Google Scholar
  12. 12.
    Stephen, D., Krebs, J.: Foraging Theory. Princeton University Press, Princeton, NJ (1986)Google Scholar
  13. 13.
    Olwal, T.O., Djouani, K., Kurien, A.M.: A survey of resource management toward 5G radio access networks. IEEE Commun. Surv. Tutor. 18(3), 1656–1686 (2016). (Third Quarter)CrossRefGoogle Scholar
  14. 14.
    Plank, M.J., James, A.: Optimal foraging: Levy pattern or process. J. R. Soc. Interface 5(26), 1077–1086 (2008)CrossRefGoogle Scholar
  15. 15.
    Nolting, B.C.: Random search models of foraging behaviour: theory, simulation and observation. PhD thesis, University of Nebraska, Nebraska (2013)Google Scholar
  16. 16.
    Olwal, T.O., Masonta, M.T., Mekuira, F.: Bio-inspired energy and channel management in distributed wireless multi-radio networks (BEACH). IET Sci. Meas. Technol. 8(6), 380–390 (2014)CrossRefGoogle Scholar
  17. 17.
    Olwal, T.O., Van Wyk, B.J., Kogeda, O.P., Mekuria, F.: FIREMAN: foraging-inspired radio-communication energy management for green multi-radio networks. In: Green Networking and Communications, pp. 29–46. CRC Press, New York (2013)Google Scholar
  18. 18.
    Yu, R.F., Huang, M., Tang, H.: Biologically inspired consensus-based spectrum sensing in mobile ad hoc networks with cognitive radios. IEEE Netw. Mag. 2(3), 26–30 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Olukayode A. Oki
    • 1
  • Thomas O. Olwal
    • 2
  • Pragasen Mudali
    • 1
  • Matthew Adigun
    • 1
  1. 1.University of ZululandRichards BayRepublic of South Africa
  2. 2.Tshwane University of TechnologyPretoriaRepublic of South Africa

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