Wireless Personal Communications

, Volume 97, Issue 3, pp 3773–3791 | Cite as

A Leader Election Protocol for Cognitive Radio Networks

  • Mahendra Kumar MurmuEmail author
  • Awadhesh Kumar Singh


Leader election is a fundamental problem of distributed computing systems. In cognitive radio network (CRN), the secondary users (SUs) are connected under the leased spectrum of primary user (also called, licensed user) and hence often called opportunistic network. The emerging trend is to maximize the channel utilization in CRN. However, the computational activities performed by the SUs depend on the activity of primary user. Thus, in general, CRN is highly dynamic and network architectures are short lived. Many applications require a leader node to carry out better coordination among the participating nodes. The CRN being a highly dynamic network, the leader election is more challenging than in other networks. The leader node coordinates the activities of SUs and regulates the appropriate channel among them keeping in view the behavioral activities of PUs, which leads to enhanced channel utilization. We propose a diffusion computation based leader election protocol for CRN. The protocol is “weakly” self stabilizing and terminating.


Cognitive radio network Leader Diffusion computation QoS Self stabilizing 


  1. 1.
    Akyildiz, I. F., Won-Yeol, 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.CrossRefGoogle Scholar
  2. 2.
    Liang, H., Lou, T., Tan, H., Wang, A. Y., & Yu, D. (2013). Complexity of connectivity in cognitive radio networks through spectrum assignment. ALGOSENSORS 2012. LNCS, 7718, 108–119.zbMATHGoogle Scholar
  3. 3.
    Akyildiz, I. F., Won-Yeol, L., & Chowdhury, K. R. (2009). CRAHNs: Cognitive radio ad hoc networks. Journal of Ad Hoc Networks, 7(5), 810–836.CrossRefGoogle Scholar
  4. 4.
    Tamhane, S. A., & Kumar, M. (2012). A token based distributed algorithm for supporting mutual exclusion in opportunistic networks. Journal of Pervasive and Mobile Computing, 8(5), 795–809.CrossRefGoogle Scholar
  5. 5.
    Bansal, T., Li, D., & Sinha, P. (2014). Opportunistic channel sharing in cognitive radio networks. IEEE Transaction on Mobile Computing, 13(4), 852–865.CrossRefGoogle Scholar
  6. 6.
    Xie, L., Jia, X., & Zho, K. (2012). QoS multicast routing in cognitive radio ad hoc networks. Journal of Communication Systems, 25(1), 30–42.CrossRefGoogle Scholar
  7. 7.
    Cesana, M., Cuomo, F., & Ekici, E. (2011). Routing in cognitive radio networks: Challenges and solutions. Lournal of Ad Hoc Networks, 9(3), 228–248.CrossRefGoogle Scholar
  8. 8.
    Sharma, S., & Singh, A. K. (2014). On termination detection in cognitive radio networks. Journal of Network management, 26(6), 499–527.CrossRefGoogle Scholar
  9. 9.
    Mittal, N., Krishnamurthy, S., Chandrasekaran, R., Venkatesan, S., & Zeng, Y. (2009). On neighbor discovery in cognitive radio networks. Journal of Parallel Distributed Computing, 69(7), 623–637.CrossRefGoogle Scholar
  10. 10.
    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. doi: 10.1016/j.jnca.2015.03.003.CrossRefGoogle Scholar
  11. 11.
    Guibène, W., & Slock, D. (2013). Cooperative spectrum sensing and localization in cognitive radio systems using compressed sensing. Journal of Sensors. Article ID 606413.Google Scholar
  12. 12.
    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.CrossRefGoogle Scholar
  13. 13.
    Dijkstra, E. W., & Scholten, C. S. (1980). Termination detection for diffusing computations. Journal of Information Processing Letters, 11(1), 1–4.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Vasudevan, S., Immerman, N., Kurose, J., Towsley, D. (2003). A leader election algorithm for mobile ad hoc networks. University of Massachusetts, Amhert, MA 01003, UMass Computer Science Techincal Report 03-01.Google Scholar
  15. 15.
    Vasudevan, S., DeCleene, B., Immerman, N., Kurose, J., & Towsley, D. (2003). Leader election algorithms for wireless ad hoc networks. In Proceedings in DARPA information survivability conference and exposition (pp. 261–272).Google Scholar
  16. 16.
    Derhab, A., & Badache, N. (2008). A self-stabilizing leader election algorithm in highly dynamic ad hoc mobile networks. IEEE Transactios on Parallel and Distributed Systems, 19(7), 926–939.CrossRefGoogle Scholar
  17. 17.
    Park, V. D., & Corson, M. S. (1997). A highly adaptive distributed routing algorithm for mobile wireless networks. In INFOCOM97 (pp. 1405–1413).Google Scholar
  18. 18.
    Bansal, T., Mittal, N., & Venkatesan, S. (2008). Leader election algorithm for multi-channel wireless networks. WASA 2008. LNCS, 5258, 310–321.Google Scholar
  19. 19.
    Arachchige, C. J. L., Venkatesan, S., & Mittal, N. (2008). An asynchronous neighbor discovery algorithm for cognitive radio networks. IEEE DySPAN, 2008, 1–5.Google Scholar
  20. 20.
    Olabiyi, O., Annamalai, A., & Qian, L. (2012). Leader election algorithm for distributed ad hoc cognitive radio networks. IEEE Consumer Communications and Networking Conference (CCNC), 2012, 859–863.CrossRefGoogle Scholar
  21. 21.
    Gotzhein, R. (1992). Temporal logic and applications—A tutorial. Journal of Computer Networks and ISDN Systems, 24(3), 203–218.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringNational Institute of TechnologyKurukshetraIndia

Personalised recommendations