From Context to Context-Awareness: Model-Based User Classification for Efficient Multicasting

  • Christian Mannweiler
  • Jörg Schneider
  • Andreas Klein
  • Hans D. Schotten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6884)


Engineering context-aware wireless networks capable of self-configuration, self-optimization, and self-healing requires a broad information base as well as sophisticated reasoning models for user and network behavior as well as for environmental conditions. The rise of smartphones and smart spaces has tremendously increased the availability of context information such as location, environmental conditions (temperature, light), or terminal capabilities. Moreover, the popularity of social networks has complemented these data with profile information about individual users. This paper outlines how available information enables self-optimization in wireless networks by designing according models. The chosen application scenario, classifying and grouping users and thus facilitating group-based multicasting, demonstrates the feasibility and the effectiveness of the described approach.


Context Information Cluster Center Multicast Tree Soft Mapping Context Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Nokia Siemens Networks: Self-Organizing Network (SON) - Introducing Nokia Siemens Networks SON Suite - an efficient and future-proof platform for SON, White Paper (2009)Google Scholar
  2. 2.
    Chalmers, R., Almeroth, K.: Modeling the Branching Characteristics and Efficiency Gains of Global Multicast Trees. In: Proceedings of IEEE INFOCOM, Anchorage, AK (2001)Google Scholar
  3. 3.
    Janic, M., Van Mieghem, P.: The Gain and Cost of Multicast Routing Trees. In: International Conference on Systems, Man and Cybernetics (IEEE SMC 2004), The Hague (2004)Google Scholar
  4. 4.
    Baumung, P., Zitterbart, M.: MAMAS - Mobility-aware Multicast for Ad-hoc Groups in Self-organizing Networks. In: Zitterbart, M., Baumung, P. (eds.) Basissoftware für drahtlose Ad-hoc- und Sensornetze, pp. 33–48. Universitaetsverlag Karlsruhe (March 2009)Google Scholar
  5. 5.
    Schneider, J., Mannweiler, C., Klein, A., Schotten, H.: Erfassung von Umgebungskontext und Kontextmanagement, vol. 14. ITG Fachtagung Mobilkommunikation, Osnabrück (2009)Google Scholar
  6. 6.
    Chen, H.: An Intelligent Broker Architecture for Pervasive Context-Aware Systems. University of Maryland, College Park (2004)Google Scholar
  7. 7.
    Klein, A., Mannweiler, C., Schneider, J., Thillen, F., Schotten, H.D.: A Concept for Context-Enhanced Heterogeneous Access Management. In: Proceedings of the Workshop on Seamless Wireless Mobility at GLOBECOM 2010, Miami, USA (December 2010)Google Scholar
  8. 8.
    Floren, P., Przybilski, M., Nurmi, P., Koolwaaij, J., Tarlano, A., Wagner, M., Luther, M., Bataille, F., Boussard, M., Mrohs, B., Lau, S.: Towards a Context Management Framework for MobiLife. In: 14th IST Mobile and Communications Summit, Dresden (2005)Google Scholar
  9. 9.
    European Framework Programme (FP) 7: Project ”C-Cast” - provide an end-to-end context-aware communication framework (2008-2010),
  10. 10.
    MacQueen, J.: Some methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  11. 11.
    Steinhaus, H.: Sur la division des corps matriels en parties. Bull. Acad. Polon. Sci. 4(12), 801–804 (1956)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Martinez, T.M., Schulten, K.J.: A neural gas network learns topologies. In: Kohonen, T., et al. (eds.) Artificial Neural Networks, pp. 397–402 (1991)Google Scholar
  13. 13.
    Fritzke, B.: Some Competitive Learning Methods. Java Paper (1997), (accessed November 11, 2010)
  14. 14.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  15. 15.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., et al. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  16. 16.
    Research Project ”G-Lab” - foster experimentally driven research to exploit future Internet technologies (2008-2011),

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Mannweiler
    • 1
  • Jörg Schneider
    • 1
  • Andreas Klein
    • 1
  • Hans D. Schotten
    • 1
  1. 1.Wireless Communications and Navigation Research GroupUniversity of KaiserslauternGermany

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