Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Data Driven Wireless Network Design: A Multi-level Modeling Approach

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless network technology keeps improving by solving problems detected in current systems and anticipating requirements for future systems. One of the possible approaches to help advancing wireless technology is to develop methods that help researchers understand the less desired behaviors that may occur in a real-world system. One such method is data driven multi-level analysis that uses the monitoring data collected from real-world networks to provide detailed insight, at several levels and/or scales, into the system behavior. This paper discusses data driven multi-level analysis, provides a proof of concept on how it can be applied and identifies challenges. The contributions of this paper are (1) the use of data driven multi-level analysis for understanding the behaviour of wireless networks and (2) the identification of open challenges and directions for future research.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. The terminology depends on the field.

  2. Open-Access Research Testbed for Next-Generation Wireless Networks (ORBIT), http://www.orbit-lab.org/.

  3. Federation for FIRE, http://www.fed4fire.eu/testbeds.html.

References

  1. Ammari, H. M., & Das, S. K. (2008). Integrated coverage and connectivity in wireless sensor networks: A two-dimensional percolation problem. IEEE Transactions on Computers, 57(10), 1423–1434.

    Article  MathSciNet  Google Scholar 

  2. Baccour, N., Koubaa, A., Mottola, L., Zuniga, M. A., Youssef, H., Boano, C. A., et al. (2012). Radio link quality estimation in wireless sensor networks: A survey. ACM Transactions on Sensor Networks (TOSN), 8(4), 34.

    Article  Google Scholar 

  3. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  4. Boano, C. A., Zúñiga, M., Brown, J., Roedig, U., Keppitiyagama, C., & Römer, K. (2014). Templab: A testbed infrastructure to study the impact of temperature on wireless sensor networks. In Proceedings of the 13th international symposium on Information processing in sensor networks (pp. 95–106). New York: IEEE Press.

  5. Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., & Leskovec, J. (2014). Can cascades be predicted? In Proceedings of the 23rd international conference on world wide web, (pp. 925–936). International World Wide Web Conference Steering Committee.

  6. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.

    Article  Google Scholar 

  7. Dousse, O., Franceschetti, M., Macris, N., Meester, R., & Thiran, P. (2006). Percolation in the signal to interference ratio graph. Journal of Applied Probability, 43(2), 552–562.

  8. Dousse, O., Franceschetti, M., & Thiran, P. (2006). On the throughput scaling of wireless relay networks. IEEE Transactions on Information Theory, 52(6), 2756–2761.

    Article  MathSciNet  MATH  Google Scholar 

  9. Franceschetti, M., & Meester, R. (2008). Random networks for communication: From statistical physics to information systems (Vol. 24). Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  10. Gluhak, A., Krco, S., Nati, M., Pfisterer, D., Mitton, N., & Razafindralambo, T. (2011). A survey on facilities for experimental internet of things research. IEEE on Communications Magazine, 49(11), 58–67.

    Article  Google Scholar 

  11. González, M. C., & Barabási, A. L. (2007). Complex networks: From data to models. Nature Physics, 3(4), 224–225.

    Article  Google Scholar 

  12. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241–254.

    Article  Google Scholar 

  13. Kawadia, V., & Kumar, P. R. (2005). A cautionary perspective on cross-layer design. IEEE on Wireless Communications, 12(1), 3–11.

    Article  Google Scholar 

  14. Klösgen, W., & Zytkow, J.M. (2002). The knowledge discovery process. In W. Klösgen & J. Zytkow (Eds.), Handbook of data mining and knowledge discovery, (pp. 10–21). Oxford: Oxford University Press, Inc.

  15. Lakkaraju, H., McAuley, J. J., & Leskovec, J. (2013). What’s in a name? understanding the interplay between titles, content, and communities in social media. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, Cambridge, Massachusetts, USA, July 8–11. Palo Alto, California: The AAAI Press.

  16. Liu, T., & Cerpa, A. E. (2011) Foresee (4c): Wireless link prediction using link features. In 2011 10th International Conference on Information Processing in Sensor Networks (IPSN), (pp. 294–305). IEEE.

  17. Liu, T., & Cerpa, A. E. (2014). Temporal adaptive link quality prediction with online learning. ACM Transactions on Sensor Networks, 10(3), 1–41. doi:10.1145/2594766.

    Google Scholar 

  18. Mehari, M. T., De Poorter, E., Couckuyt, I., Deschrijver, D., Vanhie-Van Gerwen, J., Pareit, D., et al. (2015). Efficient global optimization of multi-parameter network problems on wireless testbeds. Ad Hoc Networks, 29, 15–31.

    Article  Google Scholar 

  19. Penrose, M. D. (1995). Single linkage clustering and continuum percolation. Journal of Multivariate Analysis, 53(1), 94–109.

    Article  MathSciNet  MATH  Google Scholar 

  20. Ren, W., Zhao, Q., & Swami, A. (2014). Temporal traffic dynamics improve the connectivity of ad hoc cognitive radio networks. IEEE/ACM Transactions on Networking (TON), 22(1), 124–136.

    Article  Google Scholar 

  21. Schiffman, S. S., Reynolds, M. L., Young, F. W., & Carroll, J. D. (1981). Introduction to multidimensional scaling: Theory, methods, and applications. New York: Academic Press.

    MATH  Google Scholar 

  22. Šolc, T., Fortuna, C., & Mohorčič, M. (2015) Low-cost testbed development and its applications in cognitive radio prototyping. In M.-G. Di Benedetto, A. Fabio Cattoni, J. Fiorina, F. Bader, & L. De Nardis (Eds.), Cognitive radio and networking for heterogeneous wireless networks, (pp. 361–405). Berlin: Springer.

  23. Takens, F. (1981). Detecting strange attractors in turbulence. Berlin: Springer.

    Book  MATH  Google Scholar 

  24. Thomas, R. W., Friend, D. H., DaSilva, L., Mackenzie, A. B., et al. (2006). Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives. IEEE on Communications Magazine, 44(12), 51–57.

    Article  Google Scholar 

  25. Tukey, J. W. (1977). Exploratory data analysis. Addison Wesley series in behavioral science: quantitative methods. MA, United States: Addison Wesley.

  26. Woo, A., Tong, T., & Culler, D. (2003) Taming the underlying challenges of reliable multihop routing in sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems, (pp. 14–27). New York: ACM.

Download references

Acknowledgments

We would like to acknowledge Tomaz Šolc for sharing the data collected by the LOG-a-TEC wireless testbed monitoring system. This work was partly funded by the European Commission H2020 program under Grant Agreement Number 688116 (eWINE project), FP7 Grant Agreement Numbers 318493 (TOPOSYS project) and 612329 (Proasense project) and the IWT SBO SAMURAI project.

Author information

Authors and Affiliations

  1. Jozef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Carolina Fortuna & Primož Škraba

  2. Ghent University - iMinds, Gaston Crommerlaan 8, 9000, Ghent, Belgium

    Eli De Poorter & Ingrid Moerman

Authors
  1. Carolina Fortuna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eli De Poorter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Primož Škraba
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ingrid Moerman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carolina Fortuna.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fortuna, C., De Poorter, E., Škraba, P. et al. Data Driven Wireless Network Design: A Multi-level Modeling Approach. Wireless Pers Commun 88, 63–77 (2016). https://doi.org/10.1007/s11277-016-3242-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3242-8

Keywords

Search

Navigation