Application of Spatial Econometrics Methods in the Analysis of WLAN Performance

  • Leszek BorzemskiEmail author
  • Jakub Barański
  • Anna Kamińska-Chuchmała
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 521)


This paper presents the spatial econometric modeling to performance prediction analysis of high-density client environments in higher education. According to our knowledge, these methods were not yet used in such analysis. Particular attention was devoted to SAR (Spatial Autoregressive Model) and SEM (Spatial Error Model) models, and their comparison with a classical non-spatial regression model. We have created models for two neighbor matrices to take into account different looks at distance definition in a 3D environment. The models were compared how well they predict the number of logged users which is considered as the WLAN performance index.


Wireless local area network Performance models Performance prediction Spatial econometrics Spatial autoregressive model Spatial error model 


  1. 1.
    Anselin, L.: Thirty years of spatial econometrics. Papers in Regional Science 89(1), 3–25 (2010)CrossRefGoogle Scholar
  2. 2.
    Anselin, L., Rey, S.J.: Spatial econometrics in an age of cyber GIScience. Int. J. Geogr. Inf. Sci. 26(12), 2211–2226 (2012)CrossRefGoogle Scholar
  3. 3.
    Arbia, G., Espa, G., Giuliani, D.: Dirty spatial econometrics. Ann. Reg. Sci. 56(1), 177–189 (2016)CrossRefGoogle Scholar
  4. 4.
    Borzemski, L., Kamińska-Chuchmała, A.: Web server’s performance prediction with using spatial econometric methods. Rynek Energii 3(112), 120–124 (2014)Google Scholar
  5. 5.
    Borzemski, L. Kamińska-Chuchmała, A.: Distributed web server’s data performance processing with application of spatial econometrics models, A. Grzech et al. (eds.), Information Systems Architecture and Technology: AISC vol. 430, pp. 37–48, (2016)Google Scholar
  6. 6.
    Mirza, M., Sommers, J., Barford, P., Zhu, X.: A machine learning approach to TCP throughput prediction. IEEE/ACM Trans. Networking 18(4), 1026–1039 (2010)CrossRefGoogle Scholar
  7. 7.
    Yin, D., Yildirim, E., Kulasekaran, S., Ross, B., Kosar, T.: A data throughput prediction and optimization service for widely distributed many-task computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 899–909 (2011)CrossRefGoogle Scholar
  8. 8.
    Borzemski, L.: The use data mining to predict web performance. Cyber. Syst. 37(6), 587–608 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Borzemski, L.: Internet path behavior prediction via data mining: conceptual framework and case study. J. Univ. Comp. Sci. 13(2), 287–316 (2007)Google Scholar
  10. 10.
    Borzemski, L., Kliber, M., Nowak, Z.: Using data mining algorithms in web performance prediction. Cyber. Syst. 40(2), 176–187 (2009)CrossRefzbMATHGoogle Scholar
  11. 11.
    Borzemski, L., Starczewski, G.: Application of transfer regression to TCP throughput prediction. First Asian Conference on Intelligent Information and Database Systems (ACIIDS), IEEE Computer Society. 28–33 (2009)Google Scholar
  12. 12.
    Borzemski, L., Kamińska-Chuchmała, A.: Client-perceived web performance knowledge discovery through turning bands method. Cybern. Syst. Int. J. 43(4), 354–368 (2012)CrossRefGoogle Scholar
  13. 13.
    Borzemski, L., Kamińska-Chuchmała, A.: Spatio-temporal web performance forecasting with Sequential Gaussian Simulation method, Communications in Computer and Information Science, vol. 291, pp. 111–119. Springer, Berlin (2012)Google Scholar
  14. 14.
    Borzemski, L., Kamińska-Chuchmała, A.: Web performance forecasting with kriging method. In: Contemporary Challenges and Solutions in Applied Artificial Intelligence Studies in Computational Intelligence, vol. 489, pp. 149–154. Springer, Berlin (2013)Google Scholar
  15. 15.
    Borzemski, L., Kamińska-Chuchmała, A.: Distributed web systems performance forecasting using turning bands method. IEEE Trans. Industr. Inf. 9(1), 254–261 (2013)CrossRefGoogle Scholar
  16. 16.
    Gal, Z., Balla, T., Karsai, A. Sz.: On the WiFi interference analysis based on sensor network measurements, In: IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY 2013) Conference Proceedings, Subotica, Serbia, 26–28 September (2013)Google Scholar
  17. 17.
    Yu, H., Zeng, K., Mohapatra, P.: Measurement-Based Short-Term Performance Prediction in Wireless Mesh Networks, Proc. of 20th International Computer Communications and Networks (ICCCN), pp. 1–6, (2011)Google Scholar
  18. 18.
    Prentow, T. P., Ruiz-Ruiz, A.J. Blunck, H. Stisen, A. Kjaegaard M.B.: Spatio-temporal facility utilization analysis from the exhaustive WiFi monitoring, Pervasive and Mobile Computing, pp. 305–316, (2015)Google Scholar
  19. 19.
    Kamińska-Chuchmała, A.: Performance analysis of access points of university wireless network. Rynek Energii 1(122), 122–124 (2016)Google Scholar
  20. 20.
    GeoDa Center for Geospatial Analysis and Computation.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Leszek Borzemski
    • 1
    Email author
  • Jakub Barański
    • 1
  • Anna Kamińska-Chuchmała
    • 1
  1. 1.Department of Computer Science, Faculty of Computer Science and ManagementWrocław University of TechnologyWrocławPoland

Personalised recommendations