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Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms.
- Source :
- EAI Endorsed Transactions on Scalable Information Systems; 2021, Vol. 8 Issue 32, p1-7, 7p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20329407
- Volume :
- 8
- Issue :
- 32
- Database :
- Complementary Index
- Journal :
- EAI Endorsed Transactions on Scalable Information Systems
- Publication Type :
- Academic Journal
- Accession number :
- 152324317
- Full Text :
- https://doi.org/10.4108/eai.3-5-2021.169578