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A Multi Agent Scheme and Optimization for Big Data Management of Sensor Networks in Smart City Management

Received: 15 March 2017     Accepted: 29 March 2017     Published: 15 May 2017
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Abstract

Because of complex sensor networks in smart city management, it is very difficult to optimize the data processing from all kinds of sensors. Here a multi-agent system (MAS) is made for data processing and optimization of sensor networks in smart city management. First, the sensor network in smart city management is modeled as a self-organized and decentralized agent swarm. In the MAS, each agent’s objective value is reckoned on-line and the best agent’s update rule is on the basis of proportional control concept. Second, each agent is organized by itself to herd to the prime agent in group. And when it avoids the crash between agent and the closest obstruction/agent, it moves to a moving target. Third, to analyze the MAS’s dynamics, the eigenvalue of time-varying discrete system’s analysis is made. Besides, a guideline is put forward for application on how to adjust the parameters of MAS’s. Finally, the results of the simulation verify that the proposed self-organized swarm system is effective in the capability of migration and flocking.

Published in American Journal of Electrical and Computer Engineering (Volume 1, Issue 1)
DOI 10.11648/j.ajece.20170101.12
Page(s) 9-17
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), 2017. Published by Science Publishing Group

Keywords

Sensor Networks, Multi-Agent System, Big Data, Smart City

References
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Cite This Article
  • APA Style

    Hui Xie, Shan Jiao, Yaqian Wang, Zhengying Cai. (2017). A Multi Agent Scheme and Optimization for Big Data Management of Sensor Networks in Smart City Management. American Journal of Electrical and Computer Engineering, 1(1), 9-17. https://doi.org/10.11648/j.ajece.20170101.12

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    ACS Style

    Hui Xie; Shan Jiao; Yaqian Wang; Zhengying Cai. A Multi Agent Scheme and Optimization for Big Data Management of Sensor Networks in Smart City Management. Am. J. Electr. Comput. Eng. 2017, 1(1), 9-17. doi: 10.11648/j.ajece.20170101.12

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    AMA Style

    Hui Xie, Shan Jiao, Yaqian Wang, Zhengying Cai. A Multi Agent Scheme and Optimization for Big Data Management of Sensor Networks in Smart City Management. Am J Electr Comput Eng. 2017;1(1):9-17. doi: 10.11648/j.ajece.20170101.12

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  • @article{10.11648/j.ajece.20170101.12,
      author = {Hui Xie and Shan Jiao and Yaqian Wang and Zhengying Cai},
      title = {A Multi Agent Scheme and Optimization for Big Data Management of Sensor Networks in Smart City Management},
      journal = {American Journal of Electrical and Computer Engineering},
      volume = {1},
      number = {1},
      pages = {9-17},
      doi = {10.11648/j.ajece.20170101.12},
      url = {https://doi.org/10.11648/j.ajece.20170101.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20170101.12},
      abstract = {Because of complex sensor networks in smart city management, it is very difficult to optimize the data processing from all kinds of sensors. Here a multi-agent system (MAS) is made for data processing and optimization of sensor networks in smart city management. First, the sensor network in smart city management is modeled as a self-organized and decentralized agent swarm. In the MAS, each agent’s objective value is reckoned on-line and the best agent’s update rule is on the basis of proportional control concept. Second, each agent is organized by itself to herd to the prime agent in group. And when it avoids the crash between agent and the closest obstruction/agent, it moves to a moving target. Third, to analyze the MAS’s dynamics, the eigenvalue of time-varying discrete system’s analysis is made. Besides, a guideline is put forward for application on how to adjust the parameters of MAS’s. Finally, the results of the simulation verify that the proposed self-organized swarm system is effective in the capability of migration and flocking.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - A Multi Agent Scheme and Optimization for Big Data Management of Sensor Networks in Smart City Management
    AU  - Hui Xie
    AU  - Shan Jiao
    AU  - Yaqian Wang
    AU  - Zhengying Cai
    Y1  - 2017/05/15
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajece.20170101.12
    DO  - 10.11648/j.ajece.20170101.12
    T2  - American Journal of Electrical and Computer Engineering
    JF  - American Journal of Electrical and Computer Engineering
    JO  - American Journal of Electrical and Computer Engineering
    SP  - 9
    EP  - 17
    PB  - Science Publishing Group
    SN  - 2640-0502
    UR  - https://doi.org/10.11648/j.ajece.20170101.12
    AB  - Because of complex sensor networks in smart city management, it is very difficult to optimize the data processing from all kinds of sensors. Here a multi-agent system (MAS) is made for data processing and optimization of sensor networks in smart city management. First, the sensor network in smart city management is modeled as a self-organized and decentralized agent swarm. In the MAS, each agent’s objective value is reckoned on-line and the best agent’s update rule is on the basis of proportional control concept. Second, each agent is organized by itself to herd to the prime agent in group. And when it avoids the crash between agent and the closest obstruction/agent, it moves to a moving target. Third, to analyze the MAS’s dynamics, the eigenvalue of time-varying discrete system’s analysis is made. Besides, a guideline is put forward for application on how to adjust the parameters of MAS’s. Finally, the results of the simulation verify that the proposed self-organized swarm system is effective in the capability of migration and flocking.
    VL  - 1
    IS  - 1
    ER  - 

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

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

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

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

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