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A Data-Based Fault-Detection Model for Wireless Sensor Networks

Author

Listed:
  • Jangsik Bae

    (Research Division, Shinhan A-Tec Co. Ltd., Changwon 51793, Korea)

  • Meonghun Lee

    (Department of Agricultural Engineering, National Institute of Agricultural Sciences, Jeollabuk-do 55365, Korea)

  • Changsun Shin

    (Department of Information and Communication Engineering, Sunchon National University, Jeollanam-do 57922, Korea)

Abstract

With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO 2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

Suggested Citation

  • Jangsik Bae & Meonghun Lee & Changsun Shin, 2019. "A Data-Based Fault-Detection Model for Wireless Sensor Networks," Sustainability, MDPI, vol. 11(21), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6171-:d:283719
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    References listed on IDEAS

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    2. Christian Schader & Lukas Baumgart & Jan Landert & Adrian Muller & Brian Ssebunya & Johan Blockeel & Rainer Weisshaidinger & Richard Petrasek & Dóra Mészáros & Susanne Padel & Catherine Gerrard & Laur, 2016. "Using the Sustainability Monitoring and Assessment Routine (SMART) for the Systematic Analysis of Trade-Offs and Synergies between Sustainability Dimensions and Themes at Farm Level," Sustainability, MDPI, vol. 8(3), pages 1-20, March.
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    3. Faraz Qasim & Doug Hyung Lee & Jongkuk Won & Jin-Kuk Ha & Sang Jin Park, 2021. "Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization," Energies, MDPI, vol. 14(21), pages 1-24, November.

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