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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