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Ambient Intelligence - Software and Applications pp 147–154Cite as

Distributed Neural Computation over WSN in Ambient Intelligence

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 219))

Abstract

Ambient Intelligence (AmI) applications need information about the surrounding environment. This can be collected by means of Wireless Sensor Networks (WSN) that also analyze and build forecasts for applications. The RUBICON Learning Layer implements a distributed neural computation over WSN. In this system, measurements taken by sensors are combined by using neural computation to provide future forecasts based on previous measurements and on the past knowledge of the environment.

This work is supported in part by the EU FP7 RUBICON Project (contract no. 269914).

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References

  1. Baronti, P., et al.: Wireless sensor networks: a survey on the state of the art and the 802.15.4 and ZigBee standards. Computer Communications 30(7), 1655–1695 (2007)

    Article  Google Scholar 

  2. Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijiten, J., Burgelman, J.: Scenarios for ambient intelligence in 2010. IST Advisory Group, Tech. Rep. (February 2001)

    Google Scholar 

  3. Amato, G., Broxvall, M., Chessa, S., Dragone, M., Gennaro, C., López, R., Maguire, L., Mcginnity, T.M., Micheli, A., Renteria, A., O’Hare, G.P., Pecora, F.: Robotic uBIquitous cOgnitive network. In: Novais, P., Hallenborg, K., Tapia, D.I., Rodríguez, J.M.C. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 153, pp. 191–195. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  4. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall PTR (1998)

    Google Scholar 

  5. Moustapha, A., Selmic, R.: Wireless sensor network modeling using modified recurrent neural networks: Application to fault detection. IEEE Trans. Instrum. Meas. 57(5), 981–988 (2008)

    Article  Google Scholar 

  6. Li, Y., Parker, L.: Detecting and monitoring time-related abnormal events using a wireless sensor network and mobile robot. In: IEEE/RSJ Int. Conf. on Intel. Robots and Systems, pp. 3292–3298 (2008)

    Google Scholar 

  7. Nakano, H., Utani, A., Miyauchi, A., Yamamoto, H.: Synchronization-based data gathering scheme using chaotic pulse-coupled neural networks in wireless sensor networks. In: Proceedings of the IJCNN 2008, pp. 1115–1121 (2008)

    Google Scholar 

  8. Kulkarni, R., Förster, A., Venayagamoorthy, G.: Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys Tutorials 13(1), 68–96 (2011)

    Article  Google Scholar 

  9. Lukosevicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)

    Article  Google Scholar 

  10. Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)

    Article  MATH  Google Scholar 

  11. Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)

    Article  Google Scholar 

  12. Gallicchio, C., Micheli, A.: Architectural and markovian factors of echo state networks. Neural Networks 24(5), 440–456 (2011)

    Article  Google Scholar 

  13. Gallicchio, C., Micheli, A., Barsocchi, P., Chessa, S.: User movements forecasting by reservoir computing using signal streams produced by mote-class sensors. In: Del Ser, J., Jorswieck, E.A., Miguez, J., Matinmikko, M., Palomar, D.P., Salcedo-Sanz, S., Gil-Lopez, S. (eds.) Mobilight 2011. LNICST, vol. 81, pp. 151–168. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  14. Bacciu, D., Gallicchio, C., Micheli, A., Chessa, S., Barsocchi, P.: Predicting user movements in heterogeneous indoor environments by reservoir computing. In: Proc. of IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI), pp. 1–6 (2011)

    Google Scholar 

  15. Servicios, A.S.Y.: http://www.advanticsys.com

  16. Gay, D., et al.: The nesC language: A holistic approach to networked embedded systems. In: Proc. of the ACM SIGPLAN 2003, pp. 1–11. ACM, NY (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, University of Pisa, largo Pontecorvo 3, 56127, Pisa, Italy

    Davide Bacciu, Claudio Gallicchio, Alessandro Lenzi, Stefano Chessa, Alessio Micheli & Susanna Pelagatti

  2. ISTI-CNR, via Moruzzi 1, 56124, Pisa, Italy

    Stefano Chessa & Claudio Vairo

Authors
  1. Davide Bacciu
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  2. Claudio Gallicchio
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  3. Alessandro Lenzi
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  4. Stefano Chessa
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  5. Alessio Micheli
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  6. Susanna Pelagatti
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  7. Claudio Vairo
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Corresponding author

Correspondence to Davide Bacciu .

Editor information

Editors and Affiliations

  1. for Smart Technology and Smart Living, Stichting Smart Homes, Smart Homes Research Centre, Eindhoven, 5605, Netherlands

    Ad van Berlo

  2. , The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark

    Kasper Hallenborg

  3. , Departamento de Informática, Universidad de Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan M. Corchado Rodríguez

  4. Departamento de Informática, y Automática, Universidad de Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Dante I. Tapia

  5. , Departamento de Informática, Universidade do Minho, Campus de Gualtar, Braga, 4710-057, Portugal

    Paulo Novais

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Cite this paper

Bacciu, D. et al. (2013). Distributed Neural Computation over WSN in Ambient Intelligence. In: van Berlo, A., Hallenborg, K., Rodríguez, J., Tapia, D., Novais, P. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 219. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00566-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-00566-9_19

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00565-2

  • Online ISBN: 978-3-319-00566-9

  • eBook Packages: EngineeringEngineering (R0)

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