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Employed BPN to Multi-sensors Data Fusion for Environment Monitoring Services

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Autonomic and Trusted Computing (ATC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5586))

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Abstract

The real-time system uses a back-propagation network (BPN) with associative memory for recognition and classification in multi-sensors data fusion. This study attempts to apply classification fusion technology to the real-time signals recognition of multi-sensors data in a wireless sensor networks (WSNs) system with a node–sink mobile network structure. These wireless sensor network systems include temperature, humidity, ultraviolet, and illumination four variable measurements for environment monitoring services (EMS). Remote engineers can manage the multi-sensors data fusion using the browser, and the WSNs system then classification the data fusion database via the Internet and mobile network. Moreover, the data fields of each sensor node contain the properties and specifications of that pattern, except in the case of engineering components. The database system approach significantly improves classification data fusion system capacity. The classification fusion system examined here employs parallel computing, which increases system data fusion rate. The classification fusion system used in this work is an Internet based node–sink mobile network structure. The final phase of the classification fusion system applies database BPN technology to processing data fusion, and can solve the problem of spurious states. The system considered here is implemented on the Yang-Fen Automation Electrical Engineering Company as a case study. The experiment is continued for 4 weeks, and engineers are also used to operating the web-based classification fusion system. Therefore, the cooperative plan described above is analyzed and discussed here. Finally, these papers propose the tradition methods compare with the innovative methods.

This work was supported in part by the National Science Council Taiwan, through it’s grand no. NSC- 97-2218-E-167-001.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Chin-Yi University of Technology, 35, 215 Lane, Sec. 1, Chung Shan Road, Taiping, Taichung, Taiwan

    Wen-Tsai Sung

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  1. Wen-Tsai Sung
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Editors and Affiliations

  1. Information Security Institute, Queensland University of Technology, GPO Box 2434, QLD 4001, Brisbane, Australia

    Juan González Nieto

  2. Department of Software Engineering and Programming Languages, Institute of ComputerScience, University of Augsburg, 86135, Augsburg, Germany

    Wolfgang Reif

  3. School of Information Science and Engineering, Central South University, 410083, Changsha, Hunan Province, P. R. China

    Guojun Wang

  4. School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Brisbane, Australia

    Jadwiga Indulska

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© 2009 Springer-Verlag Berlin Heidelberg

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Sung, WT. (2009). Employed BPN to Multi-sensors Data Fusion for Environment Monitoring Services. In: González Nieto, J., Reif, W., Wang, G., Indulska, J. (eds) Autonomic and Trusted Computing. ATC 2009. Lecture Notes in Computer Science, vol 5586. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02704-8_12

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  • DOI: https://doi.org/10.1007/978-3-642-02704-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02703-1

  • Online ISBN: 978-3-642-02704-8

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