Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms

Authors

  • R. Regin Adhiyamaan College of Engineering
  • S. Suman Rajest Vels Institute of Science
  • Bhopendra Singh Amity University image/svg+xml

DOI:

https://doi.org/10.4108/eai.3-5-2021.169578

Keywords:

Wireless sensor network, Fault detection, Convolution neural network, convex hull, Naive-Bayes, performance metrics and energy efficiency

Abstract

This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted as the number of nodes increases. Second, we proposed a Naïve Bayes classifier and convolution neural network (CNN) to improve the convergence performance and find the node faults. Finally, we analyze convex hull, Naïve bayes and CNN algorithms using real-world datasets to identify and organize the faults. Simulation and experimental outcomes retain feasibility and efficiency and show that the CNN algorithm has better-identified faults than the convex hull algorithm based on performance metrics.

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Published

03-05-2021

How to Cite

1.
Regin R, Rajest SS, Singh B. Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms. EAI Endorsed Scal Inf Syst [Internet]. 2021 May 3 [cited 2024 Nov. 21];8(32):e8. Available from: https://publications.eai.eu/index.php/sis/article/view/2061