An Efficient Distributed Approach to Construct a Minimum Spanning Tree in Cognitive Radio Network

  • Deepak Rohilla
  • Mahendra Kumar MurmuEmail author
  • Shashidhar Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)


The increasing interest in cognitive radio ad hoc network (CRAHN) has driven study and development for latest approaches. The minimum spanning trees are advantageous for data broadcasting and disseminating. This article presents an associate in nursing algorithm rule for the construction of minimum spanning tree (MST) in cognitive radio network (CRN). For communication network, MST square measure is used for important network tasks like broadcast, leader election, and synchronization. We tend to be commenced our message and time restriction-based cost or weight estimation efficient distributed algorithm for construction of a minimum spanning tree. Proposed algorithm describes including facilitates of state diagram illustration. The verification demonstration of the proposed algorithm is additionally enclosed.


Distributed algorithm Cognitive radio networks Minimum spanning tree 


  1. 1.
    Murmu, M.K., Firoz, A.M., Meena, S., Jain, S.: A distributed minimum spanning tree for cognitive radio networks. IMCIP Proc. Comput. Sci. 89, 162–169 (2016). (Elsevier)CrossRefGoogle Scholar
  2. 2.
    Singh, G., Kumar, N., Verma, A.K.: Ant colony algorithms in MANETs: A review. J. Netw. Comput. Appl. 6, 1964–1972 (2012). (Elsevier)CrossRefGoogle Scholar
  3. 3.
    Sun, X., Chang, C., Su, H., Rong, C.: Novel degree constrained minimum spanning tree algorithm based on an improved multicolony ant algorithm. Math. Probl. Eng. Article ID 601782 (2015) (Hindwai Publishing)Google Scholar
  4. 4.
    Ibanez, M.L., Stutzle, T., Dorigo, M.: Ant colony optimization: A component-wise overview. IRIDIA Technical Report Series (2015)Google Scholar
  5. 5.
    Qiang, H.Z., Kai, N., Tao, Q., Tao, S., Jun, X.W., Li, G., Ru, L.J.: A bio-inspired approach for cognitive radio networks. Int. J. Chin. Sci. Bull. Theor. Wirel. Networks 57(28), 3723–3730 (2012). (Springer)Google Scholar
  6. 6.
    Song, Z., Shen, B., Zhou, Z.: Improved ant routing algorithm in cognitive radio networks. In: IEEE Conference (2009)Google Scholar
  7. 7.
    Alam, S.S., Marcenaro, L., Regazzoni, C.: Opportunistic spectrum sensing and transmissions. In: Cognitive Radio and Interference Management: Technology and Strategy: Technology and Strategy (Chap. 1) (2012)Google Scholar
  8. 8.
    Ducatelle, F., Caro, G.D., Gambardella, L.M.: Using ant agents to combine reactive and proactive strategies for routing in mobile ad hoc networks. J. Comp. Intel. Appl. 5(169), 1–15 (2005). (Researchgate)zbMATHGoogle Scholar
  9. 9.
    Fernandez-Marquez, J.L., Serugendo, G.D.M., Montagna, S.: BIO-CORE: Bio-inspired self-organising mechanisms core. In: Bio-Inspired Models of Networks, Information, and Computing Systems on LNICST, vol. 103, pp. 59–72 (2012)Google Scholar
  10. 10.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. IRIDIA Artif. Life 5(2), 137–172 (1999) (MIT Library)Google Scholar
  11. 11.
    Mao, X., Ji, H.: Biologically-inspire distributed spectrum access for cognitive radio network. In: IEEE Conference. Beijing University of Posts and Telecommunications (2010)Google Scholar
  12. 12.
    Qiang, H.Z., Kai, N., Tao, Q., Tao, S., Jun, X.W., Li, G., Ru, L.J.: A bio-inspired approach for cognitive radio networks. Int. J. Chin. Sci. Bull. Theor. Wireless Netw. 57(28)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Deepak Rohilla
    • 1
  • Mahendra Kumar Murmu
    • 1
    Email author
  • Shashidhar Kulkarni
    • 1
  1. 1.Department of Computer EngineeringNIT KurukshetraHaryanaIndia

Personalised recommendations