Advertisement

An Autonomous Access Point for Cognitive Wireless Networks

  • Bheemarjuna Reddy Tamma
  • B.S. Manoj
  • Ramesh Rao
Chapter

Abstract

In this chapter, we present an application of the Cognitive Networking paradigm to the design and development of autonomous Cognitive Access Point (CogAP) for Wi-Fi hotspots and home wireless networks. In these environments, we typically use only one AP per service provider/residence for providing wireless connectivity to the users. Here we can reduce the cost of autonomic network control by equipping the same AP with a cognitive functionality. We first present the architecture of autonomous CogAP which consists of two main modules: Traffic sensing module and cognitive controller module. The traffic sensing module uses an efficient packet sampling scheme to characterize traffic from all Wi-Fi channels with single wireless interface. The cognitive controller module consists of two submodules: traffic predictor and cognitive decision engine. The Neural Network-based traffic predictor module makes use of the historical traffic traces for traffic prediction on all channels. The cognitive decision engine makes use of traffic forecasts to dynamically decide which channel is best for CogAP to operate on. We have built a prototype CogAP device using off-the-shelf hardware components and obtained better performance with respect to state-of-the-art channel selection strategies.

Keywords

Medium Access Control Cognitive Radio Traffic Load Contention Window Channel Selection 
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.

References

  1. 1.
    R. W. Thomas, D. H. Friend, L. A. DaSilva, and A. B. MacKenzie, “Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives,” IEEE Communications Magazine, vol. 44, no. 12, pp. 51–57, 2006.CrossRefGoogle Scholar
  2. 2.
    B. S. Manoj, R. Rao, and M. Zorzi, “Architectures and protocols for next generation cognitive networking,” in Cognitive Wireless Networks: Concepts, Methodologies and Visions, M. Katz and F. Fitzek, Eds. Springer, Netherlands, 2007.Google Scholar
  3. 3.
    I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 9, pp. 2127–2159, 2006.MATHCrossRefGoogle Scholar
  4. 4.
    T. Weiss and F. Jondral, “Spectrum pooling: An innovative strategy for the enhancement of spectrum efficiency,” IEEE Communications Magazine, vol. 42, no. 3, pp. 8–14, 2004.CrossRefGoogle Scholar
  5. 5.
    J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” Ph.D. dissertation, Royal Institute of Technology (KTH), 2000.Google Scholar
  6. 6.
    M. Balazinska and P. Castro, “Characterizing mobility and network usage in a corporate wireless local-area network,” in Proc. of ACM Mobisys, 2003, pp. 303–316.Google Scholar
  7. 7.
    J. Yeo, M. Youssef, T. Henderson, and A. Agrawala, “An accurate technique for measuring the wireless side of wireless networks,” in Proc. of Workshop on Wireless Traffic Measurements and Modeling, 2005.Google Scholar
  8. 8.
    Y.-C. Cheng, J. Bellardo, P. Benkö, A. C. Snoeren, G. M. Voelker, and S. Savage, “Jigsaw: Solving the puzzle of enterprise 802.11 analysis,” SIGCOMM Computer Communication Review, vol. 36, no. 4, pp. 39–50, October 2006.CrossRefGoogle Scholar
  9. 9.
    K. C. Claffy, G. C. Polyzos, and H. W. Braun, “Application of sampling methodologies to network traffic characterization,” Computer Communication Review, vol. 23, no. 4, pp. 194–203, October 1993.CrossRefGoogle Scholar
  10. 10.
    U. Deshpande, T. Henderson, and D. Kotz, “Channel sampling strategies for monitoring wireless networks,” in Proc. of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, April 2006.Google Scholar
  11. 11.
    E. Meshkova, J. Riihijärvi, A. Achtzehn, and P. Mähönen, “Exploring simulated annealing and graphical models for optimization in cognitive wireless networks,” in Proc. of IEEE GLOBECOM, 2009, pp. 4939–4946.Google Scholar
  12. 12.
    G. Quer, H. Meenakshisundaram, B. R. Tamma, B. S. Manoj, R. Rao, and M. Zorzi, “Cognitive network adaptation using bayesian networks,” in Proc. of IEEE MILCOM, 2010.Google Scholar
  13. 13.
    N. Baldo, B. R. Tamma, B. S. Manoj, R. Rao, and M. Zorzi, “A neural network based cognitive controller for dynamic channel selection,” in Proc. IEEE ICC 2009, June 2009.Google Scholar
  14. 14.
    B. R. Tamma, N. Baldo, B. S. Manoj, and R. Rao, “Multi-channel wireless traffic sensing and characterization for cognitive networking,” in Proc. of IEEE ICC, June 2009.Google Scholar
  15. 15.
    N. Duffield, “Sampling for passive internet measurement: A review,” Statistical Science, vol. 19, no. 3, pp. 472–498, 2008.MathSciNetCrossRefGoogle Scholar
  16. 16.
    A. Sharma and E. M. Belding, “Freemac: Framework for multi-channel mac development on 802.11 hardware,” in Proc. of ACM workshop on Programmable Routers for Extensible Services of Tomorrow, 2008, pp. 69–74.Google Scholar
  17. 17.
    D. Murray, M. Dixon, and T. Koziniec, “Scanning delays in 802.11 networks,” in Proc. of International Conference on Next Generation Mobile Applications, Services and Technologies, 2007, pp. 255–260.Google Scholar
  18. 18.
    S. Kullback, in Information Theory and Statistics. Wiley, New York, NY 1959.MATHGoogle Scholar
  19. 19.
    Y. Liu, B. R. Tamma, B. S. Manoj, and R. R. Rao, “On cognitive network channel selection and the impact on transport layer performance,” in Proc. of IEEE Globecom, December 2010.Google Scholar
  20. 20.
    D. W. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” Journal of the Society for Industrial and Applied Mathematics, vol. 11, no. 2, pp. 431–441, 1963.MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis: Forecasting and Control, 3rd ed. Prentice Hall, Englewood Cliffs, NJ, 1994.MATHGoogle Scholar
  22. 22.
  23. 23.
  24. 24.
  25. 25.
  26. 26.
  27. 27.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Bheemarjuna Reddy Tamma
    • 1
  • B.S. Manoj
    • 2
  • Ramesh Rao
    • 3
  1. 1.Indian Institute of Technology HyderabadHyderabadIndia
  2. 2.Indian Institute of Space Science and Technology (IIST)TrivandrumIndia
  3. 3.University of California San DiegoLa JollaUSA

Personalised recommendations