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Wireless Networks

, Volume 25, Issue 4, pp 1567–1584 | Cite as

Predicting throughput in IEEE 802.11 based wireless networks using directional antenna

  • Saravanan KandasamyEmail author
  • Ricardo Morla
  • Patrícia Ramos
  • Manuel Ricardo
Article
  • 90 Downloads

Abstract

In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In this paper we study the relationship between the \(\textit{improved\,attacking\,case}\) metric, which captures interference, and throughput for IEEE 802.11 based network using directional antenna. The \({y}^{1/3} = a + b\ {(\text {ln}\ x)}^{3}\) model was found to best represent the relationship between the interference metric and the network throughput. We use this model to predict the performance of similar networks and decide the best configuration a network operator could use for planning his network.

Keywords

Directional antenna Prediction Regression analysis IEEE 802.11 Wireless networks 

Notes

Acknowledgements

The authors would like to thank the Fundação para a Ciência e a Tecnologia (FCT) of Ministério da Ciência, Tecnologia e Ensino Superior (MCTES), Portugal for supporting this work through grant SFRH/BD/43744/2008 and PTDC/EEA-TEL/120176/2010.

References

  1. 1.
    Liu, Q., Jiang, X., & Qiu, X. (2017). The effects of topology on throughput capacity of large scale wireless networks. Journal of Information,.  https://doi.org/10.3390/info8010032.Google Scholar
  2. 2.
    Xu, Y., Liu, J., Shen, Y., Li, X., & Jiang, X. (2017). On throughput capacity of large-scale ad hoc networks with realistic buffer constraint. Wireless Networks, 23(1), 193–204.  https://doi.org/10.1007/s11276-015-1146-2.CrossRefGoogle Scholar
  3. 3.
    Hua, Y., Huang, Y., & Garcia-Luna-Aceves, J. J. (2006). Maximizing the throughput of large ad hoc wireless networks. IEEE Signal Processing Magazine, 23(5), 84–94.  https://doi.org/10.1109/MSP.2006.1708415.CrossRefGoogle Scholar
  4. 4.
    Haenggi, M., & Ganti, R. K. (2009). Interference in large wireless networks. Foundations and Trends in Networking, 3(2), 127–248.  https://doi.org/10.1561/1300000015.CrossRefzbMATHGoogle Scholar
  5. 5.
    IEEE Standard for Info. Technology–Telecommunications and Info. Exchange between Systems LAN and MAN–Specific requirements Part 11: Wireless LAN MAC and Physical Layer (PHY) Specifications, IEEE Std 802.11-2012 (Revision of IEEE Std 802.11-2007), 2012 (pp. 1–2793).  https://doi.org/10.1109/IEEESTD.2012.6178212.
  6. 6.
    Adeyeye, M., & Gardner-Stephen, P. (2011). The Village Telco project: A reliable and practical wireless mesh telephony infra. EURASIP Journal on Wireless Communication and Network, 1, 78.  https://doi.org/10.1186/1687-1499-2011-78.CrossRefGoogle Scholar
  7. 7.
    Irwin, D., Sharma, N., Shenoy, P., & Zink, M. (2011). Towards a virtualized sensing environment. In Testbeds and Research Infrastructures. Development of NTs and Communities, Springer, Berlin (Vol. 46, pp. 133-142).  https://doi.org/10.1007/978-3-642-17851-1_10.
  8. 8.
    Anderson, E., Phillips, C., Yee, G., Sicker, D., & Grunwald, D. (2011). Challenges in deploying steerable wireless testbeds. In Testbeds and Research Infrastructures. Development of NTs and Communities, Springer, Berlin (Vol. 46, pp. 231–240).  https://doi.org/10.1007/978-3-642-17851-1_19.
  9. 9.
    Kapnadak, V., Senel, M., & Coyle, E. J. (2011). Low-complexity, distributed characterization of interferers in wireless networks. International Journal of Distributed Sensor Networks, 2011, 17.  https://doi.org/10.1155/2011/980953.Google Scholar
  10. 10.
    Karrer, R., Pescape, A., & Huehn, T. (2008). Challenges in second-generation wireless mesh networks. EURASIP Journal on Wireless Communications and Networking, 1, 2008.  https://doi.org/10.1155/2008/274790.Google Scholar
  11. 11.
    Kandasamy, S., Campos, R., Morla, R., & Ricardo, M. (2010). Using directional antennas on stub WMN: Impact on throughput, delay, and fairness. In Proceeding of the 19th International Conference on Computer Communications and NTs (ICCCN) (pp. 1–6).  https://doi.org/10.1109/ICCCN.2010.5560027.
  12. 12.
    Kandasamy, S., Campos, R., Morla, R., & Ricardo, M. (2009). Improving the performance of IEEE 802.11s NTs using directional antennas over multi-radio/multi-channel implementation - the research challenges. In Proceeding of the 4th Doctoral Symposium on Infomatics Engineering (DSIE) (pp. 1–12).Google Scholar
  13. 13.
    Kandasamy, S., Morla, R., & Ricardo, M. (2016). Power interference modeling for CSMA/CA based networks using directional antennas. Elsevier’s Journal of Computer Communications, 86, 86–98.  https://doi.org/10.1016/j.comcom.2016.01.012.CrossRefGoogle Scholar
  14. 14.
    Vlavianos, A., Law, L., Broustis, I., Krishnamurthy, S., & Faloutsos, M. (2008). Assessing link quality in IEEE 802.11 wireless NTs: Which is the right metric?. In Personal, Indoor and Mobile Radio Communications 2008. IEEE 19th International Symposium on (pp. 1–6).  https://doi.org/10.1109/PIMRC.2008.4699837.
  15. 15.
    Ho, I.-H., & Liew, S. C. (2007). Impact of power control on performance of IEEE 802.11 wireless networks. IEEE Transactions on Mobile Computing, 6(11), 1245–1258.  https://doi.org/10.1109/TMC.2007.1045.CrossRefGoogle Scholar
  16. 16.
    Papadopouli, M., Raftopoulos, E., & Shen, H. (2006). Evaluation of short-term traffic forecasting algorithms in wireless network. In Next Generation Internet Design and Engineering 2nd Conference on (pp. 8).  https://doi.org/10.1109/NGI.2006.1678229.
  17. 17.
    Chen, C., Pei, Q., & Ning, L. (2009). Forecasting 802.11 traffic using seasonal ARIMA model. In Computer Science-Technology and Applications, 2009. IFCSTA ’09. International Forum on, (Vol. 2, pp. 347–350).  https://doi.org/10.1109/IFCSTA.2009.207.
  18. 18.
    Kong, Y., Liu, X.-W., & Zhang, S. (2009). Minimax probability machine regression for wireless traffic short term forecasting. In Cognitive Wireless System., 1st UK-India International Workshop on (pp. 1–5).  https://doi.org/10.1109/UKIWCWS.2009.5749407.
  19. 19.
    Na, C., Chen, J., & Rappaport, T. (2006). Measured traffic statistics and throughput of IEEE 802.11b public WLAN hotspots with three different applications. IEEE Transactions on Wireless Communications, 5(11), 3296–3305.  https://doi.org/10.1109/TWC.2006.05043.CrossRefGoogle Scholar
  20. 20.
    Dao, N., & Malaney, R. (2007). Throughput performance of saturated 802.11g NTs. In Wireless Broadband and Ultra Wideband Communication, 2007. AusWireless 2007. The 2nd International Conferance on (pp. 31–31).  https://doi.org/10.1109/AUSWIRELESS.2007.82.
  21. 21.
    Bruno, R., Conti, M., & Gregori, E. (2009). Average-value analysis of 802.11 WLANs with persistent TCP flows. IEEE Communications Letters, 13(4), 218–220.  https://doi.org/10.1109/LCOMM.2009.080653.CrossRefGoogle Scholar
  22. 22.
    Dely, P., Kassler, A., & Sivchenko, D. (2010). Theoretical and experimental analysis of the channel busy fraction in IEEE 802.11. In Future Network and Mobile Summit (pp. 1–9).Google Scholar
  23. 23.
    Liangrui, T., & Wenjin, W. (2011). An Improved Algorithm based on NT Load Prediction for 802.11 DCF. In Natural Computation (ICNC), 2011 7th International Conferance on (Vol. 3, pp. 1466–1469).  https://doi.org/10.1109/ICNC.2011.6022299.
  24. 24.
    Jiang, L. B., & Liew, S. C. (2008). Improving throughput and fairness by reducing exposed and hidden nodes in 802.11 networks. IEEE Transactions on Mobile Computing, 7(1), 34–49.  https://doi.org/10.1109/TMC.2007.1070.CrossRefGoogle Scholar
  25. 25.
    de Carvalho, C., Gomes, D., de Souza, J., & Agoulmine, N. (2011). Multiple linear regression to improve prediction accuracy in WSN data reduction. In Network Operations and Management Symposium (LANOMS), 2011, 7th Latin American, 2011 (pp. 1–8).  https://doi.org/10.1109/LANOMS.2011.6102268
  26. 26.
    R Core Team, R. (2013). A Language and Environment for Stats. Comp., R Foundation for Statistical Comp., Vienna, Austria, ISBN 3-900051-07-0.Google Scholar
  27. 27.
    Montgomery, D., Jennings, C., & Kulahci, M. (2011). Introduction to time series analysis and forecasting, Wiley series in probability and statistics. Hoboken: Wiley. ISBN 9-781118211-50-2.Google Scholar
  28. 28.
    Gujarati, D. N. (2004). Basic econometrics. New York: The McGraw-Hill Companies. ISBN 9-780070597-93-8.Google Scholar
  29. 29.
    Ariza, C., Rugeles, L., Saavedra, D., & Guaitero, B. (2013). Measuring innovation in agricultural firms: A methodological approach. Electronic Journal of Knowledge Management, 11(3), 185–198.Google Scholar
  30. 30.
    Stonewall, A. J., & Bragg, H. M. (2012). Suspended-sediment characteristics for the Johnson Creek basin, Oregon, water years 2007–10. Scientific Investigations Report, 2012, 1–32.Google Scholar
  31. 31.
    R. Kabacoff, R in Action: Data Analysis and Graphics with R, Manning Pubs Co Series, Manning, ISBN 9-781935182-39-9 (2011).Google Scholar
  32. 32.
    Tukey, J. W. (1977). Exploratory data analysis. Boston: Addison-Wesley.zbMATHGoogle Scholar
  33. 33.
    Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques (4th ed.). Burlington: Morgan Kaufmann Publication. ISBN: 0128043571.Google Scholar
  34. 34.
    Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistical Survey, 4, 40–79.  https://doi.org/10.1214/09-SS054.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Centre for Telecommunications and Multimedia (CTM), INESC TEC - Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  2. 2.Instituto Politécnico do Porto (ISCAP) and Centre for Enterprise Systems Engineering (CESE), INESC TEC - Faculdade de EngenhariaUniversidade do PortoPortoPortugal

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