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Data Gathering Framework Based on Fog Computing Paradigm in VANETs

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10612))

Abstract

Vehicular nodes are equipped with more and more sensing units, and large amount of sensing data are generated. This makes the data gathering and monitoring is a challenging problem in VANETs. In this paper we first present an sensing and data gathering framework through the concept of fog computing in VANETs, then we propose an event-based data gathering scheme based on this framework that adaptively adjusts the threshold to upload suitable amount of data for decision making, while at the same time suppress unnecessary message transmissions. Preliminary experiments demonstrate the effectiveness of the proposed algorithm in vehicular sensing applications.

Supported by the Natural Science Foundation of China (61672441, 61303004, 61572206), the National Key Technology Support Program (2015BAH16F01), the State Scholarship Fund of China Scholarship Council (201706315020).

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Authors and Affiliations

  1. Department of Software Engineering, Software College, Xiamen University, Xiamen, 361005, China

    Yongxuan Lai, Lu Zhang & Yifan Xu

  2. College of Computer Science and Technology, Huaqiao University, Xiamen, 360000, China

    Tian Wang

  3. College of Aerospace Engineering, Xiamen University, Xiamen, 361005, China

    Fan Yang

Authors
  1. Yongxuan Lai
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  2. Lu Zhang
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  3. Tian Wang
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  4. Fan Yang
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  5. Yifan Xu
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Corresponding author

Correspondence to Yongxuan Lai .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Shaoxu Song

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Lai, Y., Zhang, L., Wang, T., Yang, F., Xu, Y. (2017). Data Gathering Framework Based on Fog Computing Paradigm in VANETs. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-69781-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69780-2

  • Online ISBN: 978-3-319-69781-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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