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Secure and reliable routing in cognitive radio networks

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Abstract

Due to the mobility of node and different spectrum availability pattern, CR networks are frequently divided into unpredictable partitions. Usually, these partitions are irregularly connected; hence, secure and reliable routing becomes major issue for these types of network. In order to overcome these issues, we propose a secure and reliable routing in CRN based on distributed Boltzmann–Gibbs learning algorithm. This algorithm is implemented for relay node selection phase. In addition, the authentication is done based on secure routing distributed Boltzmann–Gibbs learning algorithm. We consider the metrics such as trust value and total delay for the successful and reliable transmission of the packet. Also, in order to increase the reliability, we implement LDPC code at the time of relay node selection phase. The proposed code helps to cancel any kind of electronic interference and channel noise interference.

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References

  1. Qadir, J. (2016). Artificial intelligence based cognitive routing for cognitive radio networks. Artificial Intelligence Review, 45(1), 25–96.

    Article  MathSciNet  Google Scholar 

  2. Meghanathan, N. (2013). A survey on the communication protocols and security in cognitive radio networks. International Journal of Communication Networks and Information Security, 5(1), 19–38.

    Google Scholar 

  3. Amarnath prabhakaran, A., & Manikandan, A. (2013). An efficient communication and security for cognitive radio networks. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(4).

  4. Butt, M. A., & Zaman, M. (2013). Cognitive radio network: Security enhancements. Journal of Global Research in Computer Science, 4(2), 36–41.

    Google Scholar 

  5. Sanyal, S., Bhadauria, R., & Ghosh, C. (2009). Secure communication in cognitive radio networks. In 4th International Conference on Computers and Devices for Communication (CODEC 2009) (pp. 1–4). IEEE.

  6. Chen, K.-C., Chen, P.-Y., Prasad, N., Liang, Y.-C., & Sun, S. (2009). Trusted cognitive radio networking. Wireless Communications and Mobile Computing, 10(4), 467–485.

    Google Scholar 

  7. Xiao, Y., & Wang, K. (2013). Secure communication for cognitive networks based on MIMO and spreading LDPC codes. In Proceedings of the 2013 International Conference on Electronics and Communication Systems, pp. 104–112.

  8. Zhu, Q., Song, J. B., & Basar, T. (2011). Dynamic secure routing game in distributed cognitive radio networks. In 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011) (pp. 1–6). IEEE.

  9. Tang, L., & Wu, J. (2012). Research and analysis on cognitive radio network security. Wireless Sensor Network, 4(4), 120–126.

    Article  Google Scholar 

  10. Youssef, M., et al. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  MathSciNet  Google Scholar 

  11. Attar, A., et al. (2012). A survey of security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.

    Article  Google Scholar 

  12. Li, P., et al. (2012) CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In INFOCOM 2012 (pp. 100–108).

  13. Li, P., et al. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  14. Zeng, Y., et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  15. Busch, C., et al. (2012). Approximating congestion + dilation in networks via “quality of routing” games. IEEE Transactions on Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  16. Yen, Y.-S., et al. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11–12), 2238–2250.

    Article  Google Scholar 

  17. Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC. doi:10.1109/TC.2015.2417543.

    MATH  Google Scholar 

  18. Dvir, A., et al. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

    Google Scholar 

  19. Zhang, X. M., et al. (2015). Interference-based topology control algorithm for delay-constrained mobile Ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754.

    Article  MathSciNet  Google Scholar 

  20. Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC. doi:10.1109/TC.2015.2417543.

    MATH  Google Scholar 

  21. Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 182–190.

    Google Scholar 

  22. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.

    Article  Google Scholar 

  23. Song, Y., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  MathSciNet  Google Scholar 

  24. Liu, L., et al. (2015). Physarum optimization: A biology-inspired algorithm for the Steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.

    MathSciNet  MATH  Google Scholar 

  25. Yang, M., et al. (2015). Software-defined and virtualized future mobile and wireless networks: A survey. ACM/Springer Mobile Networks and Applications, 20(1), 4–18.

    Article  Google Scholar 

  26. Jiang, T., et al. (2012). QoE-driven channel allocation schemes for multimedia transmission of priority-based secondary users over cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30(7), 1215–1224.

    Article  Google Scholar 

  27. Zhou, L., et al. (2010). Context-aware middleware for multimedia services in heterogeneous networks. IEEE Intelligent Systems, 25(2), 40–47.

    Article  MathSciNet  Google Scholar 

  28. Yang, M., et al. (2015). Software-defined and virtualized future mobile and wireless networks: A survey. MONET, 20(1), 4–18.

    Google Scholar 

  29. Acampora, G., et al. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 5(2), 8.

    Google Scholar 

  30. Vasilakos, A. V., et al. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10.

    Article  Google Scholar 

  31. Yao, G., et al. (2015). Passive IP trace back: Disclosing the locations of IP spoofers from path back scatter. IEEE Transactions on Information Forensics and Security, 10(3), 471–484.

    Article  MathSciNet  Google Scholar 

  32. Yan, Z., et al. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42, 120–134.

    Article  Google Scholar 

  33. Yang, H., et al. (2014). Provably secure three-party authenticated key agreement protocol using smart cards. Computer Networks, 58, 29–38.

    Article  Google Scholar 

  34. Liu, B., et al. (2014). Toward incentivizing anti-spoofing deployment. IEEE Transactions on Information Forensics and Security, 9(3), 436–450.

    Article  Google Scholar 

  35. Jing, Q., et al. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  36. Zhou, J., et al. (2015). 4S: A secure and privacy-preserving key management scheme for cloud-assisted wireless body area network in m-healthcare social networks. Information Sciences, 314, 255–276.

    Article  Google Scholar 

  37. Fadlullah, Z. Md., et al. (2010). DTRAB: Combating against attacks on encrypted protocols through traffic-feature analysis. IEEE/ACM Transactions on Networking, 18(4), 1234–1247.

    Article  Google Scholar 

  38. Zhou, Jun, et al. (2015). Secure and privacy preserving protocol for cloud-based vehicular DTNs. IEEE Transactions on Information Forensics and Security, 10(6), 1299–1314.

    Article  Google Scholar 

  39. Liu, J., et al. (2016). Leveraging software-defined networking for security policy enforcement. Information Sciences, 327, 288–299.

    Article  Google Scholar 

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Correspondence to K. J. Prasanna Venkatesan.

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Prasanna Venkatesan, K.J., Vijayarangan, V. Secure and reliable routing in cognitive radio networks. Wireless Netw 23, 1689–1696 (2017). https://doi.org/10.1007/s11276-016-1212-4

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