Performance Analysis and Formal Verification of Cognitive Wireless Networks

  • Gian-Luca Dei Rossi
  • Lucia Gallina
  • Sabina Rossi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8168)


Cognitive Networks are a class of communication networks, in which nodes can learn how to adjust their behaviour according to the present and past network conditions. In this paper we introduce a formal probabilistic model for the analysis of wireless networks in which nodes are seen as processes capable of adapting their course of action to the environmental conditions. In particular, we model a network made of mobile nodes using the gossip protocol, and we study how the energy performance of the network varies, according to the topology changes and the transmission power. The stochastic process underlying the model is a discrete time Markov chain. We use the PRISM model checker to obtain, through Monte-Carlo simulation, numerical results for our analysis, which show how the learning-driven dynamic adjustment of transmission power can improve the energy performance while preserving connectivity.


Sensor Node Transmission Power Mobile Node Model Check Cognitive Radio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Dimakis, A.G., Sarwate, A.D., Wainwright, M.J.: Geographic Gossip: Efficient Aggregation for Sensor Networks. In: Proc. of the 5th International Conference on Information Processing in Sensor Networks, pp. 69–76. ACM (2006)Google Scholar
  2. 2.
    Donald, J.S., Yasinac, A.: Dynamic probabilistic retransmission in ad hoc networks. In: Proc. of the Int. Conference on Wireless Networks (ICWN 2004), pp. 158–164. CSREA Press (2004)Google Scholar
  3. 3.
    Fehnker, A., Gao, P.: Formal Verification and Simulation for Performance Analysis for Probabilistic Broadcast Protocols. In: Kunz, T., Ravi, S.S. (eds.) ADHOC-NOW 2006. LNCS, vol. 4104, pp. 128–141. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Fortuna, C., Mohorcic, M.: Trends in the Development of Communication Networks: Cognitive Networks. Computer Networks 53(9), 1354–1376 (2009)CrossRefGoogle Scholar
  5. 5.
    Gelenbe, E., Lent, R.: Power-aware ad hoc cognitive packet networks. Ad Hoc Networks 2(3), 205–216 (2004)CrossRefGoogle Scholar
  6. 6.
    Haas, Z.J., Halpern, J.Y., Li, L.: Gossip-based Ad Hoc Routing. IEEE/ACM Trans. Netw. 14(3), 479–491 (2006)CrossRefGoogle Scholar
  7. 7.
    Hansson, H., Jonsson, B.: A logic for reasoning about time and reliability. Formal Aspects of Computing 6(5), 512–535 (1994)zbMATHCrossRefGoogle Scholar
  8. 8.
    Mitola III., J.: Cognitive Radio - An Integrated Agent Architecture for Software Defined Radio. PhD thesis, Royal Institute of Technology, Stockholm, Sweden (2000)Google Scholar
  9. 9.
    Guo, L., Wang, J., Zhao, G.: Study on Formal Modeling and Analysis Method Oriented Cognitive Network. In: 2012 Fifth International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 402–405 (2012)Google Scholar
  10. 10.
    Kwiatkowska, M., Norman, G., Parker, D.: Prism 4.0: Verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Norman, G., Kwiatkowska, M., Parker, D.: Advances and Challenges of Probabilistic Model Checking. In: 48th Annual Allerton Conference on Communication, Control, and Computing, pp. 1691–1698. IEEE (2010)Google Scholar
  12. 12.
    Madhav, T.V., Sarma, N.V.S.N.: Maximizing Network Lifetime through Varying Transmission Radii with Energy Efficient Cluster Routing Algorithm in Wireless Sensor Networks. International Journal of Information and Electronics Engineering 2(2), 205–209 (2012)Google Scholar
  13. 13.
    Mahmoodi, T.: Energy-aware routing in the cognitive packet network. Performance Evaluation 68(4), 338–346 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Santi, P.: Topology Control in Wireless Ad Hoc and Sensor Networks. ACM Computing Surveys (CSUR) 37(2), 164–194 (2005)CrossRefGoogle Scholar
  15. 15.
    Stewart, W.J.: Probability, Markov Chains, Queues, and Simulation. Princeton University Press, UK (2009)zbMATHGoogle Scholar
  16. 16.
    Younis, O., Fahmy, S.: HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transactions on Mobile Computing 3(4), 366–379 (2004)CrossRefGoogle Scholar
  17. 17.
    Zhai, H., Fang, Y.: Physical carrier sensing and spatial reuse in multirate and multihop wireless ad hoc networks. In: Proc. of INFOCOM 2006. 25th IEEE International Conference on Computer Communications, pp. 1–12 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gian-Luca Dei Rossi
    • 1
  • Lucia Gallina
    • 1
  • Sabina Rossi
    • 1
  1. 1.Università Ca’ FoscariVeneziaItaly

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