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    Reduction of power consumption in sensor network applications using machine learning techniques

    136206_136206.pdf (2.172Mb)
    Access Status
    Open access
    Authors
    Shafiullah, G.
    Thompson, Adam
    Wolfs, Peter
    Ali, S.
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Shafiullah, G. and Thompson, A. and Wolfs, Peter and Ali, S. 2008. Reduction of power consumption in sensor network applications using machine learning techniques, in Srinivas, M. B. (ed), TENCON 2008 - 2008 IEEE Region 10 Conference, Nov 18 2008, pp. 1-6.Hyderabad, India: IEEE.
    Source Title
    TENCON 2008 - 2008 IEEE Region 10 Conference
    Source Conference
    TENCON 2008 - 2008 IEEE Region 10 Conference
    ISBN
    9781424424085
    Faculty
    Department of Electrical and Computer Engineering
    School of Engineering
    Faculty of Science and Engineering
    Remarks

    Copyright © 2008 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/26969
    Collection
    • Curtin Research Publications
    Abstract

    Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy efficient WSN application, power consumption due to raw data collection and pre-processing needs to be kept to a minimum level. In this paper, an energy-efficient data acquisition method has investigated for WSN applications using modern machine learning techniques. In an existing system, four sensor nodes were placed in each railway wagon to collect data to develop a monitoring system for railways. In this system, three sensor nodes were placed in each wagon to collect the same data using popular regression algorithms, which reduces power consumption of the system. This study was conducted using six different regression algorithms with five different datasets. Finally the best suitable algorithm have suggested based on the performance metrics of the algorithms that include: correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE)and computation complexity.

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