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Learning and Self-organization for Spatiotemporal Systems

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Abstract

This article deals with the modeling and management of spatiotemporal systems using machine learning and self-organization algorithms. Two application examples are the localization of objects from radio measurements using spatiotemporal models learned from data, and the self-organizing management of wireless multi-hop sensor networks. For both examples we show how machine learning and self-organization significantly increases accuracy and efficiency.

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Acknowledgements

Part of this work was done in the project ZESAN on reliable and energy efficient wireless sensor and actuator networks and was partially funded by German Ministry of Education and Research (BMBF) grant 01BN0712A.

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Authors and Affiliations

  1. Siemens AG Corporate Technology, 80200, Munich, Germany

    Thomas A. Runkler, Rudolf Sollacher & Andrei Szabo

Authors
  1. Thomas A. Runkler
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  2. Rudolf Sollacher
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  3. Andrei Szabo
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Correspondence to Thomas A. Runkler.

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Runkler, T.A., Sollacher, R. & Szabo, A. Learning and Self-organization for Spatiotemporal Systems. Künstl Intell 26, 269–274 (2012). https://doi.org/10.1007/s13218-012-0171-x

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  • DOI: https://doi.org/10.1007/s13218-012-0171-x

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