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Wireless Networks

, Volume 19, Issue 6, pp 1337–1347 | Cite as

CogMAC: a cognitive link layer for wireless local area networks

  • Jorge Lima de Oliveira Filho
  • Dzmitry Kliazovich
  • Fabrizio Granelli
  • Edmundo Madeira
  • Nelson L. S. da Fonseca
Article
  • 231 Downloads

Abstract

Optimization of the performance of the link layer in wireless networks is complex due to multiple parameters involved. Network management in real-time and performance adaptation are extremely challenging. In this paper, we introduce CogMAC, a cognitive link layer approach capable of tuning the network performance in highly dynamic environments. Results obtained using simulations and testbed measurements evince the superiorities of the proposed approach over existing non-adaptive techniques.

Keywords

Cognitive link layer Cognitive optimization WLAN 

Notes

Acknowledgments

The authors would like to thank FAPESP for the financial support, under grant number 2007/57336-0 and CNPq. Furthermore, the authors would like to acknowledge the funding from National Research Fund, Luxembourg in the framework of ECO-CLOUD project (C12/IS/3977641) and Marie Curie Actions of the European Commission (FP7-COFUND).

References

  1. 1.
    Ancillotti, E., Bruno, R., & Conti, M. (2008). Experimentation and performance evaluation of rate adaptation algorithms in wireless mesh networks. In: Proceedings of the 5th ACM symposium on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (pp. 7–14). New York, NY: ACM.Google Scholar
  2. 2.
    Ayari, M., Movahedi, Z., Pujolle, G., & Kamoun, F. (2009). Adma: Autonomous decentralized management architecture for manets: A simple self-configuring case study. In: Proceedings of the 2009 international conference on wireless communications and mobile computing: Connecting the world wirelessly (pp. 132–137). ACM.Google Scholar
  3. 3.
    Bononi, L., Conti, M., & Gregori, E. (2004). Runtime optimization of IEEE 802.11 wireless LANs performance. IEEE Transactions on Parallel and Distributed Systems, 15(1), 66–80.CrossRefGoogle Scholar
  4. 4.
    Cali, F., Conti, M., & Gregori, E. (2000). Dynamic tuning of the IEEE 802.11 protocol to achieve a theoretical throughput limit. IEEE/ACM Transactions on Networking (TON), 8(6), 799.CrossRefGoogle Scholar
  5. 5.
    Choi, J., Na, J., Park, K., & Kim, C. (2007) Adaptive optimization of rate adaptation algorithms in multi-rate WLANs. In: Proceeding of IEEE ICNP. Citeseer.Google Scholar
  6. 6.
    Facchini, C., & Granelli, F. (2009). Towards a model for quantitative reasoning in cognitive nodes. In: GLOBECOM Workshops, IEEE (pp. 1–6). IEEE.Google Scholar
  7. 7.
    Fortuna, C., & Mohorcic, M. (2009). Trends in the development of communication networks: Cognitive networks. Computer Networks, 53(9), 1354–1376.CrossRefGoogle Scholar
  8. 8.
    Goode, B. (2002). Voice over internet protocol (VoIP). Proceedings of the IEEE, 90(9), 1495–1517.CrossRefGoogle Scholar
  9. 9.
    Gunes, M., Hecker, M., & Bouazizi, I. (2003). Influence of adaptive RTS/CTS retransmissions on TCP in wireless and ad-hoc networks. In: Eighth IEEE international symposium on computers and communication (ISCC 2003) (pp. 855–860). IEEE.Google Scholar
  10. 10.
    IEEE. (1999). Wireless lan medium access control (mac) and physical layer (phy) specifications. IEEE Standard 802.11.Google Scholar
  11. 11.
    Iperf. Available at http://iperf.sourceforge.net/.
  12. 12.
    Jain, R. (1991). The art of computer systems performance analysis: Techniques for experimental design, measurement, simulation, and modeling. New York: Wiley.zbMATHGoogle Scholar
  13. 13.
    Love, R. (2010). Linux kernel development (3rd ed.). Boston, MA: Addison-Wesley. ISBN-10: 0672329468, ISBN-13: 9780672329463 .Google Scholar
  14. 14.
    Mjidi, M., Chakraborty, D., Nakamura, N., Koide, K., Takeda, A., & Shiratori, N. (2008). A new dynamic scheme for efficient RTS threshold handling in wireless networks. In: Proceedings of the 22nd international conference on advanced information networking and applications (pp. 734–740). IEEE Computer Society.Google Scholar
  15. 15.
    Ns2 network simulator. Available at http://www.isi.edu/nsnam/ns/.
  16. 16.
    Sutton, P., Doyle, L., & Nolan, K. (2006). A reconfigurable platform for cognitive networks. In: 1st international conference on cognitive radio oriented wireless networks and communications, 2006 (pp. 1–5). Ieee.Google Scholar
  17. 17.
    Thomas, R., DaSilva, L., & MacKenzie, A. (2005). Cognitive networks. In: New frontiers in dynamic spectrum access networks, DySPAN 2005 (pp. 352–360). IEEE.Google Scholar
  18. 18.
    Tsertou, A., & Laurenson, D. (2008). Revisiting the hidden terminal problem in a csma/ca wireless network. IEEE Transactions on Mobile Computing, 7(7), 817–831.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Xia, Q., & Hamdi, M. (2006). Contention window adjustment for IEEE 802.11 WLANs: A control-theoretic approach. In: IEEE international conference on communications, ICC’06, 9.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jorge Lima de Oliveira Filho
    • 1
  • Dzmitry Kliazovich
    • 3
  • Fabrizio Granelli
    • 2
  • Edmundo Madeira
    • 1
  • Nelson L. S. da Fonseca
    • 1
  1. 1.Institute of Computing (IC)State University of CampinasCampinasBrazil
  2. 2.Department of Information Engineering and Computer Science (DISI)University of TrentoTrentoItaly
  3. 3.Interdisciplinary Centre for Security, Reliability and TrustUniversity of LuxembourgLuxembourgLuxembourg

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