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Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy

Received: 24 October 2016     Accepted: 7 November 2016     Published: 26 December 2016
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Abstract

Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.

Published in American Journal of Networks and Communications (Volume 5, Issue 6)
DOI 10.11648/j.ajnc.20160506.12
Page(s) 128-138
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Fluid Properties Sensor, Context Awareness, Learning Fusion, Fuzzy Entropy, Uncertain Information

References
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Cite This Article
  • APA Style

    Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, et al. (2016). Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. American Journal of Networks and Communications, 5(6), 128-138. https://doi.org/10.11648/j.ajnc.20160506.12

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    ACS Style

    Zequn Zhu; Zhuye Zhang; Sansan Xiao; Jiangxia Zou; Xinyu Hou, et al. Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. Am. J. Netw. Commun. 2016, 5(6), 128-138. doi: 10.11648/j.ajnc.20160506.12

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    AMA Style

    Zequn Zhu, Zhuye Zhang, Sansan Xiao, Jiangxia Zou, Xinyu Hou, et al. Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy. Am J Netw Commun. 2016;5(6):128-138. doi: 10.11648/j.ajnc.20160506.12

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  • @article{10.11648/j.ajnc.20160506.12,
      author = {Zequn Zhu and Zhuye Zhang and Sansan Xiao and Jiangxia Zou and Xinyu Hou and Zhengying Cai},
      title = {Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy},
      journal = {American Journal of Networks and Communications},
      volume = {5},
      number = {6},
      pages = {128-138},
      doi = {10.11648/j.ajnc.20160506.12},
      url = {https://doi.org/10.11648/j.ajnc.20160506.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160506.12},
      abstract = {Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy
    AU  - Zequn Zhu
    AU  - Zhuye Zhang
    AU  - Sansan Xiao
    AU  - Jiangxia Zou
    AU  - Xinyu Hou
    AU  - Zhengying Cai
    Y1  - 2016/12/26
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ajnc.20160506.12
    DO  - 10.11648/j.ajnc.20160506.12
    T2  - American Journal of Networks and Communications
    JF  - American Journal of Networks and Communications
    JO  - American Journal of Networks and Communications
    SP  - 128
    EP  - 138
    PB  - Science Publishing Group
    SN  - 2326-8964
    UR  - https://doi.org/10.11648/j.ajnc.20160506.12
    AB  - Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.
    VL  - 5
    IS  - 6
    ER  - 

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Author Information
  • College of Computer and Information Technology, China Three Gorg

  • School of Law and Public Administration, China Three Gorges Univ

  • College of Computer and Information Technology, China Three Gorg

  • School of Foreign Languages, China Three Gorges University, Yich

  • School of Foreign Languages, China Three Gorges University, Yich

  • College of Computer and Information Technology, China Three Gorg

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