Fault detection in wireless sensor networks with deep neural networks

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Authors

  • Vasco Arone Mazibuco Thai Nguyen University of Technology
  • Nguyen Phuong Nhung Institute of Information Technology, Vietnam Academy of Science and Technology
  • Nguyen Tuan Linh (Corresponding Author) Thai Nguyen University of Technology

DOI:

https://doi.org/10.54939/1859-1043.j.mst.CSCE7.2023.27-36

Keywords:

Fault detection; Wireless sensor network; Machine learning; Recurrent neuron network; LSTM.

Abstract

This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type. We propose the use of deep neural networks, particularly Recurrent Neural Networks (RNNs), for fault detection in WSNs, focusing on temperature and humidity data. The paper emphasizes the importance of careful model selection, tuning, and thorough evaluation to enhance the accuracy and robustness of fault detection in real-world WSN applications.

References

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Published

30-12-2023

How to Cite

Vasco Arone Mazibuco, Nguyen Phuong Nhung, and Nguyen Tuan Linh. “Fault Detection in Wireless Sensor Networks With Deep Neural Networks”. Journal of Military Science and Technology, no. CSCE7, Dec. 2023, pp. 27-36, doi:10.54939/1859-1043.j.mst.CSCE7.2023.27-36.

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