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Big Data Pre-processing Techniques Within the Wireless Sensors Networks

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Book cover Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 427))

Abstract

The recent advances in sensors and communications technologies have emerged the interaction between physical resources and the need for sufficient storage volumes for keeping the continuously generated data. These storage volumes are one of the components of the Big Data to be used in future prediction processes in a broad range of fields. Usually, these data are not ready for analysis as they are incomplete or redundant. Therefore one of the current challenge related to the Big Data is how to save relevant data and discard noisy and redundant data. On the other hand, Wireless Sensor Networks (WSNs) (as a source of Big Data) use a number of techniques that significantly reduce the required data transmissions ratio. These techniques not only improve the operational lifetime of these networks but also raise the level of the refinement at the Big Data side. This article gives an overview and classifications of the data reduction and compression techniques proposed to do data pre-processing in-networks (i.e. in-WSNs). It compares and discusses which of these techniques would be adopted or modified to enhance the functionality of the WSNs while minimizing any further pre-processing at the Big Data side, thus reducing the computational and storage cost at the Big Data side.

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Acknowledgments

This paper has been elaborated in the framework of the project New creative teams in priorities of scientific research, reg. no. CZ.1.07/2.3.00/30.0055, supported by Operational Programme Education for Competitiveness and co-financed by the European Social Fund and the state budget of the Czech Republic and supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme, and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VSB-Technical University of Ostrava.

Author information

Authors and Affiliations

  1. Arab Academy for Science, Technology, and Maritime Transport, Cairo, Egypt

    Mohamed Mostafa Fouad

  2. IT4Innovations, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Mohamed Mostafa Fouad, Tarek Gaber, Nour E. Oweis & Vaclav Snasel

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. FEECS, Department of Computer Science, VŠB-TUO, Ostrava, Czech Republic

    Nour E. Oweis & Vaclav Snasel

  5. Aqaba University of Technology (AUT), Aqaba, Jordan

    Maamoun Ahmed

  6. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Mohamed Mostafa Fouad & Tarek Gaber

Authors
  1. Mohamed Mostafa Fouad
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  2. Tarek Gaber
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  3. Maamoun Ahmed
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  4. Nour E. Oweis
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  5. Vaclav Snasel
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Corresponding author

Correspondence to Mohamed Mostafa Fouad .

Editor information

Editors and Affiliations

  1. Sci. Network for Innovation & Rec. Exell, Machine Intelligence Research Labs (MIR, Auburn, Washington, USA

    Ajith Abraham

  2. Membre du Groupe Efrei, ESIGETEL, Villejuif, France

    Katarzyna Wegrzyn-Wolska

  3. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  4. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  5. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

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Fouad, M.M., Gaber, T., Ahmed, M., Oweis, N.E., Snasel, V. (2016). Big Data Pre-processing Techniques Within the Wireless Sensors Networks. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_61

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  • DOI: https://doi.org/10.1007/978-3-319-29504-6_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29503-9

  • Online ISBN: 978-3-319-29504-6

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