Fuzzy-Based Reliable Spectrum Tree Formation for Efficient Communication in Cognitive Radio Ad Hoc Network

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


In view of the scarce major radio resource vis-a-vis to resolve the bottleneck experienced in the present scenario of revolutionary technology, researches are under progress in the domain of cognitive radio ad hoc network (CRAHN). In this context, spectrum sensing followed by reliable as well as efficient spectrum detection and its effective utilization has been a main feature to achieve the quality at par with the intelligibility during communication in CRAHN. In this paper, performance of spectrum sensing has been discussed and tried to meet the challenges for a secured communication. A new approach has been formulated here using spectrum tree formation in cognitive radio ad hoc network environment at each intermediate node in between source and destination points. Analyzing this novel concept using fuzzy spectrum tree, the proposed model proves to perform better in cognitive radio ad hoc network environment in terms of throughput with minimum overheads.


CRAHN Spectrum analysis Spectrum sensing Fuzzy based spectrum detection Spectrum tree 


  1. 1.
    Federal Communications Commission: Facilitating opportunities forflexible, efficient, and reliable spectrum use employing cognitive radio technologies, FCC Report, ET Docket, (2003) 03–322Google Scholar
  2. 2.
    Mitola, J.: Cognitive radio for flexible multimedia communications. In: IEEE International Workshop on Mobile Multimedia Communications (MoMuC ‘99), pp. 3–10 (1999)Google Scholar
  3. 3.
    Lassila, P., Penttinen, A.: Survey on Performance Analysis of Cognitive Radio Networks. COMNET Department, Helsinki University of Technology, Finland (2009)Google Scholar
  4. 4.
    Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRefGoogle Scholar
  5. 5.
    Li, C., Li, C.: Dynamic channel selection algorithm for cognitive radios. In: 4th IEEE International Conference on Circuits and Systems for Communications (ICCSC 2008), pp. 175–178 (2008)Google Scholar
  6. 6.
    Chen, T., Zhang, H., Maggio, G.M., Chlamtac, I.: CogMesh: a cluster-based cognitive radio network. In: Proceedings of IEEE DySPAN (2007)Google Scholar
  7. 7.
    Issariyakul, T., Pillutla, L.S., Krishnamurthy, V.: Tuning radio resource in an overlay cognitive radio network for TCP: greed isn’t good. In: IEEE Communication Magazin, pp. 57–63 (2009)Google Scholar
  8. 8.
    VIT RESEARCH REPORT VIT-R-02219-08Google Scholar
  9. 9.
    Khalid, Q., Hasari, C., Muneer, M., Sabit, E.: Performance analysis of ad hoc dispersed spectrum cognitive radio networks over fading channels, URASIP J. Wireless Commun. Networking (2011)Google Scholar
  10. 10.
    Hampshire, F.: Cognitive radio technology: a study. QinetiQ Ltd Cody Technology Park, vol. 1(1.1) (2007)Google Scholar
  11. 11.
    Clancy, T., Walker, B.: Predictive dynamic spectrum access. In: SDR Forum Conference (2006)Google Scholar
  12. 12.
    Akyildiz, I.F., Lee, W., Vuran, M., Mohanty, S.: Next generation/dynamic spectrum access/cognitive radio wireless networks a survey. Comput. Netw. 50(13), 2127–2159 (2006)CrossRefMATHGoogle Scholar
  13. 13.
    Wong, Y.F., Wong, W.C.: A fuzzy-decision-based routing protocol for mobile ad hoc networks. In: 10th IEEE International Conference on Network, pp. 317–322 (2002)Google Scholar
  14. 14.
    Raju, G.V.S., Hernandez, G., Zou, Q.: Quality of service routing in ad hoc networks. IEEE Wireless Commun. Networking Conf. 1, 263–265 (2000)CrossRefGoogle Scholar
  15. 15.
    Pedrycz, W., Gomide, F.: An introduction to fuzzy sets: analysis and design (complex adaptive systems). MIT Press, Cambridge (1998)MATHGoogle Scholar
  16. 16.
    Buckley, J.J., Eslami, E., Esfandiar, E.: An introduction to fuzzy logic and fuzzy sets (advances in soft computing). Physica Verlag (2002)Google Scholar
  17. 17.
    Rout, A., Sethi, S.: Throughput analysis of spectrum in cognitive radio ad hoc network. Int. J. Appl. Innov. Eng. Manage. (IJAIEM) 2(8) (2013)Google Scholar
  18. 18.
    Zhu, G.M., Akyildiz, I.F., Kuo, G.S.: STOD-RP: A spectrum-tree based on-demand routing protocol for multi-hop cognitive radio networks. In: IEEE Global Telecommunications Conference. IEEE GLOBECOM, pp. 1–5 (2008)Google Scholar
  19. 19.
    Rout, A., Sethi, S., Banerjee, P.K.: Fuzzy-based reliable and efficient communication in cognitive radio ad hoc network. In: Proceeding of IEEE International Conference on Control, Instrumentation, Energy and Communication (CIEC-2014), Kolkota (2014)Google Scholar
  20. 20.
    Ns-2 Manual, Internet Draft: http://www.isi.edu/nsnam/ns/nsdocumentation.html (2009)
  21. 21.

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of ETC EngineeringIGIT, SaranagDhenkanalIndia
  2. 2.Department of CSEAIGIT, SaranagDhenkanalIndia
  3. 3.Department of ETC EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations