Fault Tolerant Routing Protocol in Cognitive Radio Networks

  • Santosh KumarEmail author
  • Awadhesh Kumar Singh


The primary objective of the cognitive radio network (CRN) is to improve the spectrum utilization and achieve significant packet delivery ratio (PDR). However, CRN is high failure prone due to the node mobility and primary user (PU) interference. This article presents a robust routing protocol to handle failure during data transmission in CRN. In this protocol, each node maintains a list of candidates for next hop and orders them based on common channels. Most of the existing routing protocols trigger the rerouting on detection of the link failure, while our protocol uses the alternate link (forwarding node) to transmit data rather than rerouting. Thus, it achieves significant PDR with a controlled end to end delay. Finally, the performance of protocol has been evaluated through extensive simulation experiments. The simulation results conform that our protocol is robust and guarantee higher data delivery despite PU interference as compared to existing protocols.


Cognitive radio networks Routing protocols Delay Efficient data delivery 



  1. 1.
    Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50, 2127–2159.CrossRefzbMATHGoogle Scholar
  2. 2.
    Li, C., Liu, W., Li, J., Liu, Q., & Li, C. (2013). Aggregation based spectrum allocation in cognitive radio networks. In 2013 IEEE/CIC international conference on communications in China-workshops (CIC/ICCC), pp. 50–54.Google Scholar
  3. 3.
    Nekovee, M. (2009). Quantifying the availability of TV white spaces for cognitive radio operation in the UK. In IEEE international conference on communications workshops, 2009. ICC workshops 2009, pp. 1–5.Google Scholar
  4. 4.
    Kamruzzaman, S. M., Jeong, D. G. (2010). Routing protocols for cognitive radio networks: A survey. Journal of Information Industrial Engineering, 16, 153–169.Google Scholar
  5. 5.
    Marina, M. K., & Das, S. R. (2001). Ad hoc on-demand multipath distance vector (AOMDV) routing. In Proceedings of ICNP.Google Scholar
  6. 6.
    Rath, M., Pattanayak, B. K., & Pati, B. (2016). Energy efficient MANET protocol using cross layer design for military applications. Defence Science Journal, 66, 146–150.CrossRefGoogle Scholar
  7. 7.
    Ding, L., Melodia, T., Batalama, S. N., Matyjas, J. D., & Medley, M. J. (2010). Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology, 59, 1969–1979.CrossRefGoogle Scholar
  8. 8.
    Wang, J., Yue, H., Hai, L., & Fang, Y. (2017). Spectrum-aware anypath routing in multi-hop cognitive radio networks. IEEE Transactions on Mobile Computing, 16, 1176–1187.CrossRefGoogle Scholar
  9. 9.
    Caleffi, M., Akyildiz, I. F., & Paura, L. (2012). OPERA: Optimal routing metric for cognitive radio ad hoc networks. IEEE Transactions on Wireless Communications, 11, 2884–2894.Google Scholar
  10. 10.
    Liu, Y., Cai, L. X., & Shen, X. S. (2012). Spectrum-aware opportunistic routing in multi-hop cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30, 1958–1968.CrossRefGoogle Scholar
  11. 11.
    Tang, F., Tang, C., Yang, Y., Yang, L. T., Zhou, T., Li, J., et al. (2017). Delay-minimized routing in mobile cognitive networks for time-critical automation applications. IEEE Transactions on Industrial Informatics, 13(3), 1398–1409.CrossRefGoogle Scholar
  12. 12.
    Jin, X., Zhang, R., Sun, J., & Zhang, Y. (2014). TIGHT: A geographic routing protocol for cognitive radio mobile ad hoc networks. IEEE Transactions on Wireless Communications, 13, 4670–4681.CrossRefGoogle Scholar
  13. 13.
    Kamruzzaman, S., Kim, E., Jeong, D. G., & Jeon, W. S. (2012). Energy-aware routing protocol for cognitive radio ad hoc networks. IET Communications, 6, 2159–2168.CrossRefGoogle Scholar
  14. 14.
    Zareei, M., Mohamed, E. M., Anisi, M. H., Rosales, C. V., Tsukamoto, K., & Khan, M. K. (2016). On-demand hybrid routing for cognitive radio ad-hoc network. IEEE Access, 4, 8294–8302.CrossRefGoogle Scholar
  15. 15.
    Chen, Y.-W., Liao, P.-Y., & Wang, Y.-C. (2016). A channel-hopping scheme for continuous rendezvous and data delivery in cognitive radio network. Peer-to-Peer Networking and Applications, 9, 16–27.CrossRefGoogle Scholar
  16. 16.
    Barve, S. S., & Kulkarni, P. (2014). Multi-agent reinforcement learning based opportunistic routing and channel assignment for mobile cognitive radio ad hoc network. Mobile Networks and Applications, 19, 720–730.CrossRefGoogle Scholar
  17. 17.
    Ping, S., Aijaz, A., Holland, O., & Aghvami, A. (2015). SACRP: A spectrum aggregation-based cooperative routing protocol for cognitive radio ad-hoc networks. IEEE Transactions on Communications, 63(6), 2015–2030.CrossRefGoogle Scholar
  18. 18.
    Chowdhury, K. R., & Akyildiz, I. F. (2011). CRP: A routing protocol for cognitive radio ad hoc networks. IEEE Journal on Selected Areas in Communications, 29, 794–804.CrossRefGoogle Scholar
  19. 19.
    Basak, S., & Acharya, T. (2015). Joint power allocation and routing in outage constrained cognitive radio ad hoc networks. Mobile Networks and Applications, 20, 636–648.CrossRefGoogle Scholar
  20. 20.
    Liu, X., Li, B., & Liu, G. (2018). Simultaneous cooperative spectrum sensing and energy harvesting in multi-antenna cognitive radio. Mobile Networks and Applications, 23(2), 263–271.CrossRefGoogle Scholar
  21. 21.
    Hu, H., Zhang, H., & Yu, H. (2014). Efficient spectrum sensing with minimum transmission delay in cognitive radio networks. Mobile Networks and Applications, 19, 487–501.CrossRefGoogle Scholar
  22. 22.
    Xing, X., Jing, T., Cheng, W., Huo, Y., Cheng, X., & Znati, T. (2014). Cooperative spectrum prediction in multi-PU multi-SU cognitive radio networks. Mobile Networks and Applications, 19, 502–511.CrossRefGoogle Scholar
  23. 23.
    Liu, X., Chen, K., Yan, J., & Na, Z. (2016). Optimal energy harvesting-based weighed cooperative spectrum sensing in cognitive radio network. Mobile Networks and Applications, 21, 908–919.CrossRefGoogle Scholar
  24. 24.
    Supraja, P., & Pitchai, R. (2017). Spectrum prediction in cognitive radio with hybrid optimized neural network. Mobile Networks and Applications.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringNational Institute of TechnologyKurukshetraIndia

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