American Journal of Neural Networks and Applications

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A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks

Received: 26 October 2016    Accepted: 09 November 2016    Published: 16 January 2017
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Abstract

In wireless sensor networks, the load imbalance will seriously affect the performance of the whole networks, such as local traffic overload, congestion, idle resources and other problems. In this paper, a novel fuzzy neural network algorithm is proposed to solve the problem. First, the problem of load balancing in context-aware wireless sensor networks is analyzed, and the mathematical model is built up. Second, a load balancing optimization algorithm is brought combing neural network and fuzzy theory, and the whole process is also illustrated including learning, association, recognition and information processing. Third, through analyzing and studying a case, a load balancing problem is solved by simulation and comparison to show the potential of the proposed method. Last, some interesting conclusions and future work are indicated at the end of the paper.

DOI 10.11648/j.ajnna.20160202.11
Published in American Journal of Neural Networks and Applications (Volume 2, Issue 2, December 2016)
Page(s) 6-16
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), 2024. Published by Science Publishing Group

Keywords

Context-Aware, Optimization Algorithm, Fuzzy Neural Networks, Load Balancing, Wireless Sensor Networks

References
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Author Information
  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

  • School of Law and Public Administration, China Three Gorges University, Yichang, China

  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

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

  • College of Computer and Information Technology, China Three Gorges University, Yichang, China

Cite This Article
  • APA Style

    Wencheng Zuo, Hui Xie, Yuchi Lin, Hui Hu, Zhengying Cai. (2017). A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks. American Journal of Neural Networks and Applications, 2(2), 6-16. https://doi.org/10.11648/j.ajnna.20160202.11

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    ACS Style

    Wencheng Zuo; Hui Xie; Yuchi Lin; Hui Hu; Zhengying Cai. A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks. Am. J. Neural Netw. Appl. 2017, 2(2), 6-16. doi: 10.11648/j.ajnna.20160202.11

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    AMA Style

    Wencheng Zuo, Hui Xie, Yuchi Lin, Hui Hu, Zhengying Cai. A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks. Am J Neural Netw Appl. 2017;2(2):6-16. doi: 10.11648/j.ajnna.20160202.11

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  • @article{10.11648/j.ajnna.20160202.11,
      author = {Wencheng Zuo and Hui Xie and Yuchi Lin and Hui Hu and Zhengying Cai},
      title = {A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks},
      journal = {American Journal of Neural Networks and Applications},
      volume = {2},
      number = {2},
      pages = {6-16},
      doi = {10.11648/j.ajnna.20160202.11},
      url = {https://doi.org/10.11648/j.ajnna.20160202.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajnna.20160202.11},
      abstract = {In wireless sensor networks, the load imbalance will seriously affect the performance of the whole networks, such as local traffic overload, congestion, idle resources and other problems. In this paper, a novel fuzzy neural network algorithm is proposed to solve the problem. First, the problem of load balancing in context-aware wireless sensor networks is analyzed, and the mathematical model is built up. Second, a load balancing optimization algorithm is brought combing neural network and fuzzy theory, and the whole process is also illustrated including learning, association, recognition and information processing. Third, through analyzing and studying a case, a load balancing problem is solved by simulation and comparison to show the potential of the proposed method. Last, some interesting conclusions and future work are indicated at the end of the paper.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - A Load Balancing Optimization Algorithm for Context-Aware Wireless Sensor Networks Based on Fuzzy Neural Networks
    AU  - Wencheng Zuo
    AU  - Hui Xie
    AU  - Yuchi Lin
    AU  - Hui Hu
    AU  - Zhengying Cai
    Y1  - 2017/01/16
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajnna.20160202.11
    DO  - 10.11648/j.ajnna.20160202.11
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
    SP  - 6
    EP  - 16
    PB  - Science Publishing Group
    SN  - 2469-7419
    UR  - https://doi.org/10.11648/j.ajnna.20160202.11
    AB  - In wireless sensor networks, the load imbalance will seriously affect the performance of the whole networks, such as local traffic overload, congestion, idle resources and other problems. In this paper, a novel fuzzy neural network algorithm is proposed to solve the problem. First, the problem of load balancing in context-aware wireless sensor networks is analyzed, and the mathematical model is built up. Second, a load balancing optimization algorithm is brought combing neural network and fuzzy theory, and the whole process is also illustrated including learning, association, recognition and information processing. Third, through analyzing and studying a case, a load balancing problem is solved by simulation and comparison to show the potential of the proposed method. Last, some interesting conclusions and future work are indicated at the end of the paper.
    VL  - 2
    IS  - 2
    ER  - 

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