Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms

Regin, R. and Rajest, S. Suman and Singh, Bhopendra (2021) Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms. EAI Transactions on Scalable Information Systems. e8. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Wireless sensor network, Fault detection, Convolution neural network, convex hull, Naive-Bayes, performance metrics and energy efficiency
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 26 Jul 2021 15:12
Last Modified: 26 Jul 2021 15:12
URI: https://eprints.eudl.eu/id/eprint/5173

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