Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Modeling 802.11 AP usage through daily keep-alive event counts

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless and in particular 802.11 is one of the major technologies for accessing the Internet at home, in coffee shops, enterprises, university campuses, and other public places. While most recent works on modeling wireless sites focuses on user mobility and user residing time, this paper presents and compares a number of models for characterizing access point (AP) usage including time-dependent models that considers week structure usage. Moreover, rather than looking at throughput we focus on daily counts of keep-alive events that mobile devices generate every 15 min while they are connected to the wireless network. We model both daily event counts and above–below AP event counts average binary indicator. Our models are trained and evaluated on data collected from Porto hotspot of Eduroam, the European academic wireless network. The models we present are generative, in the sense they can be used to generate synthetic daily event counts for a single AP or a collection of APs. We provide standard cross-validation comparison of models using the log-likelihood of the models on training and test data. We conclude that significant improvements in AP usage modeling capability can be observed by considering (1) simple time dependency (2) week-days/week-ends usage structure and (3) individual day’s usage; whereas extending the complexity of time dependency ordering of AP’s usage samples does not show significant improvements for daily event count models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Afanasyev, M., Chen, T., Voelker, G. M., & Snoeren, A. C. (2008). Analysis of a mixed-use urban WiFi network: When metropolitan becomes neapolitan. In Proceedings of the 8th ACM SIGCOMM conference on internet measurement, 2008.

  2. Dartmouth’s Crawdad website. http://crawdad.cs.dartmouth.edu/. Last accessed May 2011.

  3. Hsu, W., Dutta, D., & Helmy, A. (2007). Mining behavioral groups in large wireless LANs. In Proceedings of the 13th annual ACM international conference on mobile computing and networking, 2007.

  4. Hsu, W.-j., & Helmy, A. (2010). On nodal encounter patterns in wireless LAN traces. IEEE Transaction on Mobile Computing, 9(11), 1563–1577.

    Google Scholar 

  5. Kumar, U., & Helmy, A. (2010). Extract mining social features from WLAN traces: A gender-based case study. In Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems (MSWiM), 2010.

  6. Boc, M., Fladenmuller, A., & Dias de Amorim, M. (2007). Towards self-characterization of user mobility patterns. In Mobile and wireless communications summit, 2007.

  7. Song, L., Kotz, D., & Jain, R. (2006). Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Transactions on Mobile Computing, 5(12), 1633–1679.

    Google Scholar 

  8. Sendra, S., Garcia, M., Turro, C., & Lloret, J. (2009). People mobility behaviour study in a university campus using WLANs. In Third international conference on mobile ubiquitous computing, systems, services and technologies, 2009.

  9. Chen, Y.-C., & Rosensweig, E. Group detection in mobility traces. In Proceedings of the 6th international wireless communications and mobile computing conference, 2010.

  10. Jain, R., Lelescu, D., & Balakrishnan, M. (2005). Model T: An empirical model for user registration patterns in a campus wireless LAN. In MobiCom05.

  11. Lelescu, D., Kozat, U. C., Jain, R., & Balakrishnan, M. (2006). Model T++: An empirical joint space-time registration model. In MobiHoc06.

  12. Hsu, W.-J., Spyropoulos, T., Psounis, K., & Helmy, A. (2009). Modeling spatial and temporal dependencies of user mobility in wireless mobile networks. IEEE/ACM Transactions on Networking, 17(5), 1564–1577.

    Google Scholar 

  13. Tuduce, C., & Gross, T. (2005). A mobility model based on WLAN traces and its validation. In IEEE INFOCOM, 2005.

  14. Resta, G., & Santi, P. (2008). WiQoSM: An integrated QoS-aware mobility and user behavior model for wireless data networks. IEEE Transactions on Networking Mobile Computing, 7(2), 187–198.

    Google Scholar 

  15. Lee, J.-K., & Hou, J. C. (2006). Modeling steady-state and transient behaviors of user mobility: Formulation, analysis, and application. In MobiHoc06.

  16. Henderson, T., Kotz, D., & Abyzov, I. (2008). The changing usage of a mature campus-wide wireless network. Journal of Computer Networks, 52, 2690–2712.

    Article  MATH  Google Scholar 

  17. Hernandez-Campos, F., & Papadopouli, M. (2005). A comparative measurement study of the workload of wireless access points in campus networks. In 16th annual IEEE international symposium on personal indoor and mobile radio communications, 2005.

  18. Chinchilla, F., Lindsey, M., & Papadopouli, M. (2004). Analysis of wireless information locality and association patterns in a campus. In IEEE INFOCOM, 2004.

  19. Papadopoulil, M., Shen, H., & Spanakis, M. (2005). Characterizing the duration and association patterns of wireless access in acampus. In 13th European IEEE conference on next generation wireless and mobile communications and services, 2005.

  20. Balachandran, A., Voelker, G., Bahl, P., & Rangan, V. (2002). Characterizing user behavior and network performance in a public wireless LAN. In Joint international conference on measurement and modeling of computer systems, 2002.

  21. Wang, T., Xing, G., Li, M., & Jia, W. (2010). Efficient WiFi deployment algorithms based on realistic mobility characteristics. In IEEE 7th international conference on mobile adhoc and sensor systems, 2010.

  22. Ergin, M. A., Ramachandran, K., & Gruteser, M. (2008). An experimental study of inter-cell interference effects on system performance in unplanned wireless LAN deployments. Journal of Computer Networks, 52, 2728–2744.

    Article  MATH  Google Scholar 

  23. Balazinska, M., & Castro, P. (2003). Characterizing mobility and network usage in a corporate wireless local-area network. In The first international conference on mobile systems, applications, and services (MobiSys), 2003.

  24. Phillips, C., & Singh, S. (2008). An empirical activity model for WLAN users. In IEEE INFOCOM, 2008.

  25. Ghosh, A., Jana, R., Ramaswami, V., Rowland, J., & Shankaranarayanan, N. K. Modeling and characterization of large-scale Wi-Fi traffic in public hot-spots. In IEEE INFOCOM, 2011.

  26. Campos, F. H., Karaliopoulos, M., Papadopouli, M., & Shen, H. Spatial temporal modeling of traffic workload in a campus WLAN. In Proceedings of the 2nd annual ACM international workshop on Wireless Internet (WICON), 2006.

  27. Tzagkarakis, G., Papadopouli, M., & Tsakalides, P. (2009). Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN. Journal of Performance Evaluation, 66, 3–5.

    Article  Google Scholar 

  28. Massa, D., & Morla, R. (2010). Modeling 802.11 AP usage through daily keep-alive even count. In IEEE 13th international conference on network based information systems (NBiS), 2010.

Download references

Acknowledgments

The authors acknowledge the support of FCT (Fundação para a Ciência e a Tecnologia) with the Associate Laboratory contract INESC TEC under grant SFRH/BD/69824/2010.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dossa Massa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Massa, D., Morla, R. Modeling 802.11 AP usage through daily keep-alive event counts. Wireless Netw 19, 1005–1022 (2013). https://doi.org/10.1007/s11276-012-0514-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-012-0514-4

Keywords

Navigation