An Adaptive and Dynamic Simulation Framework for Incremental, Collaborative Classifier Fusion

  • Gernot Bahle
  • Andreas Poxrucker
  • George Kampis
  • Paul Lukowicz
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 674)

Abstract

To investigate incremental collaborative classifier fusion techniques, we have developed a comprehensive simulation framework. It is highly flexible and customizable, and can be adapted to various settings and scenarios. The toolbox is realized as an extension to the NetLogo multi-agent based simulation environment using its comprehensive Java-API. The toolbox has been integrated in two different environments, one for demonstration purposes and another, modeled on persons using realistic motion data from Zurich, who are communicating in an ad hoc fashion using mobile devices.

Keywords

Ad hoc communication Incremental classifier fusion Collaborative computing 

References

  1. 1.
    Franke, T., Kampis, G., Lukowicz, P.: Leveraging human mobility in smartphone based ad-hoc information distribution in crowd management scenarios. Submitted to MobiSys 2015. http://www.sigmobile.org/mobisys/2015/
  2. 2.
    Kampis, G., Kantelhardt, J.W., Kloch, K., Lukowicz, P.: Analytical and simulation models for collaborative localization. J. Comput. Sci. 6(1), 1–10 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kampis, G., Lukowicz, P.: Collaborative knowledge fusion by ad-hoc information distribution in crowds. In: International Conference on Computational Science, ICCS 2015, ReyjavikGoogle Scholar
  4. 4.
    Kampis, G., Lukowicz, P.: Collaborative localization as a paradigm for incremental knowledgefusion. In: 5th IEEE CogInfoCom 2014 Conference (2014). http://coginfocom.hu/conference/CogInfoCom14/downloads/Program_CogInfoCom_2014_final.pdf
  5. 5.
    Kloch, K., Lukowicz, P., Fischer, C.: Collaborative PDR localisation with mobile phones. In: Proceedings of the 2011 15th Annual International Symposiumon Wearable Computers, ISWC 11, pp. 37–40. IEEE Computer Society, Washington, DC, USA (2011)Google Scholar
  6. 6.
    Poxrucker, A., Bahle, G., Lukowicz, P.: Towards a real-world simulator for collaborative distributed learning in the scenario of urban mobility. In: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW), pp. 44–48 (2014)Google Scholar
  7. 7.
    Ruta, D., Gabrys, B.: An overview of classifier fusion methods. Comput. Inf. Syst. 7(1), 1–10 (2000)Google Scholar
  8. 8.
    Tisue, S., Wilensky, U.: Netlogo: a simple environment for modeling complexity. In: International Conference on Complex Systems, pp. 16–21 (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Gernot Bahle
    • 1
  • Andreas Poxrucker
    • 1
  • George Kampis
    • 1
    • 2
    • 3
  • Paul Lukowicz
    • 1
  1. 1.DFKI German Research Centre for Artificial IntelligenceKaiserslauternGermany
  2. 2.Eötvös UniversityBudapestHungary
  3. 3.ITMO UniversitySt. PetersburgRussia

Personalised recommendations