Advertisement

Wireless Personal Communications

, Volume 109, Issue 4, pp 2221–2238 | Cite as

Impact of Secondary User Block on the TCP Throughput in Cognitive Radio Sensor Networks

  • Mohammad Mehdi HassaniEmail author
  • Reza Berangi
Article
  • 24 Downloads

Abstract

The cognitive radio sensor network (CRSN) has emerged as a promising solution to overcome spectrum under-utilization and spectrum scarcity problems in a resource-constrained wireless sensor network. In CRSN, TCP has to cope with a new type of packet loss due to the primary users arrival, known as secondary user blocking loss (SBL), otherwise It leads to significant TCP throughput degradation. In this paper, two main contributions are provided on the modeling of SBL and throughput evaluation of transport layer protocol for CRSN. First, it is identified two main factors of SBL and the probability of them is modeled by a discrete-time Markov chain. Second, a new congestion control algorithm is proposed to distinguish between actual congestion from the wrong congestion due to the SBL by considering the dynamic nature of CRSN. The obtained results through proposed model are verified using the COGNS framework based on NS2, which is a simulation framework for cognitive radio sensor networks. The proposed algorithm is compared with some of the well-known transport protocol TFRC-CR, OHTP and TCP Reno. The results confirm that our proposed algorithm is the best among them.

Keywords

Cognitive radio sensor network Rate based congestion control Secondary user blocking probability Discrete-time Markov chain (DTMC) TCP 

Notes

References

  1. 1.
    Ozger, M., Fadel, E. A., & Akan, O. B. (2016). Event-to-sink spectrum-aware clustering in mobile cognitive radio sensor networks. IEEE Transactions on Mobile Computing,15(9), 2221–2233.  https://doi.org/10.1109/tmc.2015.2493526.CrossRefGoogle Scholar
  2. 2.
    Ozger, M., & Akan, O. B. (2016). On the utilization of spectrum opportunity in cognitive radio networks. IEEE Communications Letters,20(1), 157–160.  https://doi.org/10.1109/lcomm.2015.2504103.CrossRefGoogle Scholar
  3. 3.
    Esmaeelzadeh, V., Hosseini, E. S., Berangi, R., & Akan, O. B. (2016). Modeling of rate-based congestion control schemes in cognitive radio sensor networks. Ad Hoc Networks Journal,36(1), 177–188.  https://doi.org/10.1016/j.adhoc.2015.06.009.CrossRefGoogle Scholar
  4. 4.
    Joshi, G. P., Nam, S. Y., & Kim, S. W. (2013). Cognitive radio wireless sensor networks: Applications, challenges and research trends. Sensors,13(9), 11196–11228.  https://doi.org/10.3390/s130911196.CrossRefGoogle Scholar
  5. 5.
    Kondareddy, Y. R., & Agrawal, P. (2009). Effect of dynamic spectrum access on transport control protocol performance. In GLOBECOM—IEEE global telecommunications conference.  https://doi.org/10.1109/glocom.2009.5426234.
  6. 6.
    Luo, C., Yu, F. R., Ji, H., & Leung, V. C. (2010). Cross-layer design for TCP performance improvement in cognitive radio networks. IEEE Transactions on Vehicular Technology,59(5), 2485–2495.  https://doi.org/10.1109/tvt.2010.2041802.CrossRefGoogle Scholar
  7. 7.
    Raspopovic, M., Thompson, C., & Chandra, K. (2005). Performance models for wireless spectrum shared by wideband and narrowband sources. In IEEE military communication conference ‘05, October 17–20 (pp. 1–6).  https://doi.org/10.1109/milcom.2005.1605910.
  8. 8.
    Tang, P. K., Chew, Y. H., Ong, L. C., & Haldar, M. K. (2006). Performance of secondary radios in spectrum sharing with prioritized primary access. Military Communications Conference.  https://doi.org/10.1109/MILCOM.2006.302214.CrossRefGoogle Scholar
  9. 9.
    Xing, Y., Chandramouli, R., Mangold, S., & Sai Shankar, N. (2006). Dynamic spectrum access in open spectrum wireless networks. IEEE Journal on Selected Areas in Communications,24(3), 626–637.  https://doi.org/10.1109/JSAC.2005.862415.CrossRefGoogle Scholar
  10. 10.
    Ashish, M., & Chauhan., R. (2014). Transport control protocol for cognitive radio ad hoc network. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE),3(4), 371–376.Google Scholar
  11. 11.
    Di Felice, M., Chowdhury, K. R., Kim, W., Kassler, A., & Bononi, L. (2011). End-to-end protocols for cognitive radio ad hoc networks: An evaluation study. Performance Evaluation,68(9), 859–875.  https://doi.org/10.1016/j.peva.2010.11.005.CrossRefGoogle Scholar
  12. 12.
    Slingerland, A. M. R., Pawełczak, P., Prasad, R. V., Lo, A., & Hekmat, R. (2007). Performance of transport control protocol over dynamic spectrum access links. In 2007 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks (pp. 486–495).  https://doi.org/10.1109/dyspan.2007.71.
  13. 13.
    Di Felice, M., Chowdhury, K. R., & Bononi, L. (2009). Modeling and performance evaluation of transmission control protocol over cognitive radio ad hoc networks. In Proceedings of the ACM conference on modeling, analysis and simulation of wireless and mobile systems (pp. 4–12).  https://doi.org/10.1145/1641804.1641809.
  14. 14.
    Tang, P. K., Chew, Y. H., Ong, L. C., & Haldar, M. K. (2007). Performance of secondary radios in spectrum sharing with prioritized primary access. In Proceedings—IEEE military communications conference MILCOM.  https://doi.org/10.1109/milcom.2006.302214.
  15. 15.
    Amjad, M. F., Aslam, B., & Zou, C. (2013). Transparent cross-layer solutions for throughput boost in cognitive radio networks. In Proceedings of IEEE CCNC 2013 (pp. 580–586).  https://doi.org/10.1109/CCNC.2013.6488502.
  16. 16.
    Wang, J., Huang, A., Wang, W., Zhang, Z., & Lau, V. K. N. (2014). On the transmission opportunity and TCP throughput in cognitive radio networks. International Journal of Communication Systems,27(2), 303–321.  https://doi.org/10.1002/dac.2362.CrossRefGoogle Scholar
  17. 17.
    Li, G., Hu, Z., Zhang, G., Zhao, L., Li, W., & Tian, H. (2011). Cross-layer design for energy efficiency of TCP traffic in cognitive radio networks. In Proceedings of IEEE VTC fall (pp. 1–5).Google Scholar
  18. 18.
    Wang, X., Sun, X., Zhao, C., & Zhou, Z. (2010). TCP-CReno-TCP enhancement using cross-layer for cognitive radio networks. In Proceedings of IET AIAI (pp. 37–40).  https://doi.org/10.1109/icbnmt.2010.5705147.
  19. 19.
    Kumar, A., & Shin, K. G. (2012). DSASync: Managing end-to-end connections in dynamic spectrum access wireless LANs. IEEE/ACM Transactions on Networking,20(4), 1068–1081.  https://doi.org/10.1109/TNET.2011.2178264.CrossRefGoogle Scholar
  20. 20.
    Luo, C., Yu, F. R., Ji, H., & Leung, V. C. M. (2010). Cross-layer design for TCP performance improvement in cognitive radio networks. IEEE Transactions on Vehicular Technology,59(5), 2485–2495.  https://doi.org/10.1109/TVT.2010.2041802.CrossRefGoogle Scholar
  21. 21.
    Wang, J., Huang, A., & Wang, W. (2012). TCP throughput enhancement for cognitive radio networks through lower-layer configurations. In Proceedings of IEEE PIMRC (pp. 1424–1429).  https://doi.org/10.1109/pimrc.2012.6362571.
  22. 22.
    Khalife, H., Conan, V., Leguay, J., & Spyropoulos, T. (2013). Point to multipoint transport in multichannel wireless environments. In Proceedings of IEEE WCNC (pp. 1404–1409).  https://doi.org/10.1109/wcnc.2013.6554769.
  23. 23.
    Chowdhury, K. R., Felice, M. D., & Akyildiz, I. F. (2009). TP-CRAHN: A transport protocol for cognitive radio ad-hoc networks. In Proceedings of IEEE INFOCOM (pp. 2482–2490).  https://doi.org/10.1109/infcom.2009.5062176.
  24. 24.
    Khalife, H., Seddar, J., Conan, V., & Leguay, J. (2013). Validation of a point to multipoint cognitive radio transport protocol over GNU radio testbed. In Proceedings of IFIP/IEEE wireless days (pp. 1–6).  https://doi.org/10.1109/wd.2013.6686523.
  25. 25.
    Al-Ali, A. K., & Chowdhury, K. R. (2013). TFRC-CR: An equation-based transport protocol for cognitive radio networks. Elsevier Ad Hoc Networks,11(6), 1836–1847.  https://doi.org/10.1016/j.adhoc.2013.04.007.CrossRefGoogle Scholar
  26. 26.
    Zikria, Y. B., Nosheen, S., Ishmanov, F., & Kim, S. W. (2015). Opportunistic hybrid transport protocol (OHTP) for cognitive radio ad hoc sensor networks. Sensors,15, 31672–31686.  https://doi.org/10.3390/s151229871.CrossRefGoogle Scholar
  27. 27.
    Lee, W.-Y., & Akyildiz, I. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications,7(10), 3845–3857.  https://doi.org/10.1109/T-WC.2008.070391.CrossRefGoogle Scholar
  28. 28.
    Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network,23(4), 34–40.CrossRefGoogle Scholar
  29. 29.
    Federal Communications Commission. (2015). Notice of proposed rulemaking and order (FCC 03-222). http://web.cs.ucdavis.edu/~liu/289I/Material/FCC-03-322A1.pdf. Accessed December 11, 2015.
  30. 30.
    Mathis, M., Semke, J., Mahdavi, J., & Ott, T. (1997). The macroscopic behaviour of the TCP congestion avoidance algorithm. ACM SIGCOMM Computer Communications Review,27(3), 67–82.  https://doi.org/10.1145/263932.264023.CrossRefGoogle Scholar
  31. 31.
    Slingerland, A. M. R., Pawelczak, P., Prasad, R. V., Lo, A., & Hekmat, R. (2007). Performance of transport control protocol over dynamic spectrum access links. In 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks 2007. DySPAN 2007 (pp. 486–495).Google Scholar
  32. 32.
    Esmaeelzadeh, V., Berangi, R., Sebt, S. M., Hosseini, E. S., & Parsinia, M. (2013). CogNS: A simulation framework for cognitive radio networks. Wireless Personal Communications,72(4), 28492865.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer EngineeringIslamic Azad University, Ayatollah Amoli BranchAmolIran
  2. 2.Department of Computer EngineeringIran University of Science and TechnologyTehranIran

Personalised recommendations