Due to the unreliable nature of wireless links, sensor nodes frequently encounter degraded performance or lack of connection. This paper provides a real-time status monitoring and channel error diagnosis method in a wireless sensor network. It uses a poll and echo procedure to efficiently collect some physical layer and link layer statistics from both transmitter side and receiver side. A machine learning approach (k-nearest neighbor) is used for failure isolation. Our scheme involves learning the normal and anomalous behavior of the network via continued observation, and classifying future events and observations as normal or different classes of errors based on past experiences. With implementation on IEEE 802.15.4 SoC, our experimental results show that the proposed framework can diagnose the major wireless transmission errors with high accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine Learning Based Channel Error Diagnostics in Wireless Sensor Networks


    Contributors:
    Yi, Su (author) / Wang, Hao (author) / Tian, Jun (author) / Xue, Wenqian (author) / Wang, Leifei (author) / Fan, Xiaojing (author) / Matsukura, Ryuichi (author)


    Publication date :

    2017-06-01


    Size :

    442690 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Model based error correction for wireless sensor networks

    Mukhopadhyay, S. / Panigrahi, D. / Dey, S. | IEEE | 2004


    Random forests for industrial device functioning diagnostics using wireless sensor networks

    Elghazel, Wiem / Medjaher, Kamal / Zerhouni, Noureddine et al. | IEEE | 2015



    Low power channel coding for Wireless Sensor Networks

    Abughalieh, N / Steenhaut, K / Nowé, A | IEEE | 2010


    MACHINE LEARNING APPLICATIONS FOR SHOCK TRAIN DIAGNOSTICS

    Chin, Jared / Gamba, Mirko | TIBKAT | 2021