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Connectivity Estimation in Wireless Sensor Networks

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (ruSMART 2016, NEW2AN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 9870))

Abstract

The connectivity of a wireless sensor network is one of the most important indicators of the network capabilities. This article describes the characteristics of connectivity and proposed a method of its estimation for wireless sensor networks. We use the Erdos-Renyi’s model for random graphs as the connectivity model. The applicability of this model to connectivity estimation of the network with different number of nodes was investigated. A possibility of using UAVs for improving the connectivity in wireless sensor network was also considered.

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Acknowledgment

The reported study was supported by RFBR, research project No. 15 07-09431a “Development of the principles of construction and methods of self-organization for Flying Ubiquitous Sensor Networks”.

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Authors and Affiliations

  1. Saint-Petersburg State University of Telecommunications, Saint Petersburg, Russia

    Ilhom Nurilloev, Alexander Paramonov & Andrey Koucheryavy

Authors
  1. Ilhom Nurilloev
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  2. Alexander Paramonov
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  3. Andrey Koucheryavy
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Corresponding author

Correspondence to Alexander Paramonov .

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Editors and Affiliations

  1. Tampere University of Technology , Tampere, Finland

    Olga Galinina

  2. FRUCT Oy , Helsinki, Finland

    Sergey Balandin

  3. Tampere University of Technology , Tampere, Finland

    Yevgeni Koucheryavy

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Nurilloev, I., Paramonov, A., Koucheryavy, A. (2016). Connectivity Estimation in Wireless Sensor Networks. In: Galinina, O., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. ruSMART NEW2AN 2016 2016. Lecture Notes in Computer Science(), vol 9870. Springer, Cham. https://doi.org/10.1007/978-3-319-46301-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-46301-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46300-1

  • Online ISBN: 978-3-319-46301-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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