CSIP—Cuckoo Search Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks

  • J. RamkumarEmail author
  • R. Vadivel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 556)


Cognitive radio (CR) is viewed as the empowering innovation of the dynamic spectrum access (DSA) model which is imagined to take care of the present spectrum scarcity issue by encouraging the contraption of new remote administrations. Cognitive devices have the similar proficiency of CR and the expedient network that they form dynamically is called cognitive radio ad hoc networks (CRAHNs). Due to assorted qualities in channels, routing becomes a critical undertaking job in CRAHN. Minimizing the end-to-end delay is one of the major difficult tasks in CRAHNs, where the transmission of packets passes on every hop of routing path. In this paper, a new reactive multicast routing protocol namely cuckoo search inspired protocol (CSIP) is proposed to reduce the overall end-to-end delay, which progressively reduces the congestion level on various routing path by considering the spectrum accessibility and the service rate of each hop in CRAHNs. Simulations are demonstrated using NS2 tool and the results proved that the proposed routing protocol CSIP significantly outperforms better than other baseline schemes in minimizing end-to-end delay in CRAHNs.


Cognitive Radio CRAHN Bio-inspired Cuckoo Routing CSIP 


  1. 1.
    Jaime Lloret Mauri., Kayhan Zrar Ghafoor., Danda B. Rawat., Javier Manuel Aguiar Perez.: Cognitive Networks: Applications and Deployments, CRC Press. (2015). 203–235.Google Scholar
  2. 2.
    P. Reviriego., S. Pontarelli., J.A. Maestro.: Energy Efficient Exact Matching for Flow Identification with Cuckoo Affinity Hashing. IEEE Communications Letters, vol. 18, no. 5. (2014). 885–888.Google Scholar
  3. 3.
    M. Grissa., A.A. Yavuz., B. Hamdaoui.: Cuckoo Filter-Based Location-Privacy Preservation in Database-Driven Cognitive Radio Networks. 2015 World Symposium on Computer Networks and Information Security (WSCNIS), Hammamet. (2015). 1–7.Google Scholar
  4. 4.
    J.Y. Chang., S.H. Liao, S.L. Wu., C.T. Lin.: A Hybrid of Cuckoo Search and Simplex Method for Fuzzy Neural Network Training. 2015 IEEE 12th International Conference on Networking, Sensing and Control (ICNSC), Taipei. (2015). 13–16.Google Scholar
  5. 5.
    E. Herazo., M. Quintero., J. Candelo., J. Soto., J. Guerrero.: Optimal Power Distribution Network Reconfiguration using Cuckoo Search. 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS), Sharjah. (2015). 1–6.Google Scholar
  6. 6.
    S.S. Taheri., S.J. Seyed-Shenava., M. Modiri-Delshad.: Transmission Network Expansion Planning Under Wind Farm Uncertainties using Cuckoo Search Algorithm. 3rd IET International Conference on Clean Energy and Technology (CEAT) 2014, Kuching. (2014) 1–6.Google Scholar
  7. 7.
    Xin-She Yang., Suash Deb.: Multiobjective Cuckoo Search For Design Optimization. Journal Computers and Operations Research, Volume 40 Issue 6. (2013). 1616–1624.Google Scholar
  8. 8.
    B. Nancharaiah., B. Chandra Mohan.: Hybrid optimization using Ant Colony Optimization and Cuckoo Search in MANET routing. 2014 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur. (2014). 1729–1734.Google Scholar
  9. 9.
    Ehsan Teymourian., Vahid Kayvanfar., GH.M. Komaki., M. Zandieh.: Enhanced Intelligent Water Drops and Cuckoo Search Algorithms for Solving the Capacitated Vehicle Routing Problem. Information Sciences, Vol 334–335. (2016). 354–378.Google Scholar
  10. 10.
    Sonia Goyal., Manjeet Singh Patterh.: Wireless Sensor Network Localization Based on Cuckoo Search Algorithm. Journal Wireless Personal Communications: An International Journal, Volume 79 Issue 1. (2014). 223–234.Google Scholar
  11. 11.
    S.B. Raha., T. Som., K.K. Mandal., N. Chakraborty.: Cuckoo Search Algorithm based Optimal Reactive Power Dispatch. 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), Calcutta. (2014), 412–416.Google Scholar
  12. 12.
    Xiaocong Jin., Rui Zhang., Jingchao Sun., Yanchao Zhang.: TIGHT: A Geographic Routing Protocol for Cognitive Radio Mobile Ad Hoc Networks. IEEE Transactions on Wireless Communications, vol. 13, no. 8. (2014). 4670–4681.Google Scholar
  13. 13.
    Xin-She Yang., Suash Deb.: Cuckoo Search via Levy Flights. World congress on nature and biologically inspired computing’NABIC-2009, vol 4. Coimbatore, (2009). 210–214.Google Scholar
  14. 14.
    Xin-She Yang., Suash Deb.: “Engineering Optimisation by Cuckoo Search”. International Journal Math Modell Numer Optim 1(4). (2010). 330–343.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceVLB Janakiammal College of Arts and Science (Affiliated to Bharathiar University)CoimbatoreIndia
  2. 2.Department of Information TechnologyBharathiar UniversityCoimbatoreIndia

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