Research Article
Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms
@ARTICLE{10.4108/eai.3-5-2021.169578, author={R. Regin and S. Suman Rajest and Bhopendra Singh}, title={Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms}, journal={EAI Endorsed Transactions on Scalable Information Systems: Online First}, volume={}, number={}, publisher={EAI}, journal_a={SIS}, year={2021}, month={5}, keywords={Wireless sensor network, Fault detection, Convolution neural network, convex hull, Naive-Bayes, performance metrics and energy efficiency}, doi={10.4108/eai.3-5-2021.169578} }- R. Regin
S. Suman Rajest
Bhopendra Singh
Year: 2021
Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms
SIS
EAI
DOI: 10.4108/eai.3-5-2021.169578
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.
Copyright © 2021 R. Regin et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.