Skip to main content
SpringerLink
Log in

Speedy leader election to avoid application discontinuity in cognitive radio networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

In cognitive radio networks (CRN), secondary user (SU) nodes operate in primary users’ unused spectrum bands. Thus, the link between SU nodes may be short lived due to (largely) unpredictable appearance of PU despite SU’s being capable of multiple channel access. Further, the nodes may suffer frequent disconnection due to node mobility and spectrum mobility. A network is considered reliable if SU’s have been carefully synchronized to ensure timely use of the available channel(s). Many computing applications require a leader node to carry out efficient coordination among the participant nodes. In this paper, we propose a diffusion computation based leader election protocol for CRN. We apply a handover mechanism for control transfer. Our handover mechanism can avoid the premature termination of some applications and thus enhances system throughput. The objective is to find maximum Id node as leader in a connected component. In extent, we validate our algorithm using simulation results and include illustration for correctness proof.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Akyildiz, I. F., Won, Y. L., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Journal of Computer Networks, 50(13), 2127–2159.

    Article  Google Scholar 

  2. Akyildiz, I. F., Won, Y. L., & Chowdhury, K. R. (2009). CRAHNs: Cognitive radio ad hoc networks. Journal Ad Hoc Networks, 7(5), 810–836.

    Article  Google Scholar 

  3. Liang, H., Lou, T., Tan, H., Wang, A. Y., & Yu, D. (2013). Complexity of connectivity in cognitive radio networks through spectrum assignment. In Springer ALGOSENSORS, LNCS 7718 (pp. 108–119).

  4. Mittal, N., Krishnamurthy, S., Chandrasekaran, R., Venkatesan, S., & Zeng, Y. (2009). On neighbor discovery in cognitive radio networks. Journal of Parallel and Distributed Computing, 69(7), 623–637.

    Article  Google Scholar 

  5. Khan, A. A., Rehmani, M. H., & Saleem, Y. (2015). Neighbor discovery in traditional wireless networks and cognitive radio networks: Basics, taxonomy, challenges and future research directions. Journal of Network and Computer Applications, 52, 173–190.

    Article  Google Scholar 

  6. Arachchige, C. J. L. (2012). Algorithms for neighbor discovery and broadcasting in cognitive radio networks. Ph.D. Doctral Thesis, University of Texas at Dallas.

  7. Xie, L., Jia, X., & Zho, K. (2012). QoS multicast routing in cognitive radio ad hoc networks. Journal of Communication Systems, 25(1), 30–42.

    Article  Google Scholar 

  8. Cesana, M., Cuomo, F., & Ekici, E. (2011). Routing in cognitive radio networks: Challenges and solutions. Journal Ad Hoc Networks, 9(3), 228–248.

    Article  Google Scholar 

  9. Guibène, W., & Slock, D. (2013). Cooperative spectrum sensing and localization in cognitive radio systems using compressed sensing. Journal of Sensors., 2013, https://doi.org/10.1155/2013/606413.

  10. Buzluca, F., & Kahraman, B. (2015). An efficient and adaptive channel handover procedure for cognitive radio networks. Journal of Wireless Communications and Mobile Computing, 15(3), 442–458.

    Article  Google Scholar 

  11. Lu, D., Huang, X., Weile, Z., & Fan, J. (2014). Interference-aware spectrum handover for cognitive radio networks. Journal of Wireless Communications and Mobile Computing, 14(11), 1099–1112.

    Article  Google Scholar 

  12. Ma, B., Xie, X., & Liao, X. (2014). PSHO-HF-PM: An efficient proactive spectrum handover mechanism in cognitive radio networks. Journal of Wireless Personal Communications, 79(3), 1679–1701.

    Article  Google Scholar 

  13. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handover. Journal of Cluster Computing, 20(2), 1505–1515.

    Article  Google Scholar 

  14. Sharma, S., & Singh, A. K. (2014). On termination detection in cognitive radio networks. Journal of Network Management, 24(6), 499–527.

    Article  Google Scholar 

  15. Junior, P. R. W., Fonseca, M., Munaretto, A., Viana, A. C., & Ziviani, A. (2011). ZAP: A distributed channel assignment algorithm for cognitive radio networks. Journal of Wireless Communications and Networking, 27, 1–11.

    Google Scholar 

  16. Bansal, T., Li, D., & Sinha, P. (2014). Opportunistic channel sharing in cognitive radio networks. IEEE Transactions on Mobile Computing, 13(4), 852–865.

    Article  Google Scholar 

  17. Gardellin, V., Das, S. K., & Lenzini, L. (2013). Coordination problem in cognitive wireless mesh networks. Journal of Pervasive and Mobile Computing, 9(1), 18–34.

    Article  Google Scholar 

  18. Murmu, M. K., & Singh, A. K. (2017). A leader election protocol for cognitive radio networks. Journal of Wireless Personal Communications, 97(3), 3773–3791.

    Article  Google Scholar 

  19. Vasudevan, S., Immerman, N., Kurose, J., & Towsley, D. (2003). A leader election algorithm for mobile ad hoc networks. University of Mass, Amhert, MA 01003, UMass CST Report.

  20. Dijkstra, E. W., & Scholten, C. S. (1980). Termination detection for diffusing computations. Journal of Information Processing Letters, 11(1), 1–4.

    Article  Google Scholar 

  21. Vasudevan, S., DeCleene, B., Immerman, N., Kurose, J., & Towsley, D. (2003). Leader election algorithms for wireless ad hoc networks. In IEEE ISCE (pp. 261–272).

  22. Derhab, A., & Badache, N. (2008). A self-stabilizing leader election algorithm in highly dynamic ad hoc mobile networks. IEEE Transactions on Parallel and Distributed Systems, 19(7), 926–939.

    Article  Google Scholar 

  23. Boukerche, A., & Abrougui, K. (2006). An efficient leader election protocol for mobile networks. In ACM WCMC (pp. 1129–1134).

  24. Park, S. H., Lee, T. G., Seo, H. S., Kwon, S. J., & Han, J. H. (2009). An election protocol in mobile ad hoc distributed systems. In IEEE IT: New generation (pp. 628–633)

  25. Zhang, G., Kuang, X., Chen, J., & Zhang, Y. (2009). Design and implementation of a leader election algorithm in hierarchy mobile ad hoc network. In IEEE CE (pp. 263–268).

  26. Bansal, T., Mittal, N., & Venkatesan, S. (2008). Leader election algorithm for multi-channel wireless networks. In Springer WASA, LNCS (Vol. 5258, pp. 310–321).

  27. Arachchige, C. J. L., Venkatesan, S., Mittal, N. (2008) An asynchronous neighbor discovery algorithm for cognitive radio networks. In IEEE DySPAN (pp. 1–5).

  28. Olabiyi, O., Annamalai, A., & Qian, L. (2012). Leader election algorithm for distributed ad hoc cognitive radio networks. In IEEE CCNC (pp. 859–863).

  29. Murmu, M. K. (2016). System-related characteristic-based leader election protocol for cognitive radio networks. In S. C. Satapathy, A. Joshi, N. Modi, & N. Pathak (Eds.), Ahamedabad, Gujarat, India. ICT4SD Springer AISC series (pp. 1–8). Berlin: Springer.

    Google Scholar 

  30. Gotzhein, R. (1992). Temporal logic and applications—A tutorial. Journal of Computer Networks and ISDN Systems, 24(3), 203–218.

    Article  Google Scholar 

  31. Chang, E. J. H. (1982). Echo algorithms: Depth parallel operations on general graphs. IEEE Transactions on Software Engineering, 8(4), 391–401.

    Article  Google Scholar 

  32. Raymond, K. (1989). Tree-based algorithm for distributed mutual exclusion. ACM Transactions on Computer System, 7(1), 61–77.

    Article  Google Scholar 

  33. Felice, M. D., Chodhury, K. R., Kim, W., Kasseler, A., & Bononi, L. (2011). End-to-end protocols for cognitive radio ad hoc networks: An evaluation study. International Journal of Performance Evaluation, 68(9), 859–875.

    Article  Google Scholar 

  34. Khattab, A., & Bayoumi, M. A. (2015). An overview of IEEE standardization efforts for cognitive radio networks. In IEEE international symposium on circuits and systems (ISCAS) (pp. 1–4).

  35. Sherman, M., Mody, A. N., Martinez, R., Rodriguez, C., & Reddy, R. (2009). IEEE standards supporting cognitive radio and networks, dynamic spectrum access, and coexistence. IEEE Communications Magazine, 46(7), 0163–6804.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Mahendra Kumar Murmu & Awadhesh Kumar Singh

Authors
  1. Mahendra Kumar Murmu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Awadhesh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahendra Kumar Murmu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Murmu, M.K., Singh, A.K. Speedy leader election to avoid application discontinuity in cognitive radio networks. Telecommun Syst 70, 349–364 (2019). https://doi.org/10.1007/s11235-018-0480-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-018-0480-y

Keywords

Search

Navigation