Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network

Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power ava...

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Bibliographic Details
Published in:Electronics (Basel) 2022-05, Vol.11 (10), p.1609
Main Authors: Gnanavel, S., Sreekrishna, M., Mani, Vinodhini, Kumaran, G., Amshavalli, R. S., Alharbi, Sadeen, Maashi, Mashael, Khalaf, Osamah Ibrahim, Abdulsahib, Ghaida Muttashar, Alghamdi, Ans D., Aldhyani, Theyazn H. H.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Classifiers
Communication
Data loss
Data mining
Datasets
Decision making
Deep learning
Failure
Fault detection
Faults
Hazardous areas
Machine learning
Multilayer perceptrons
Neural networks
Nodes
Sensors
Support vector machines
Technology assessment
Wireless networks
Wireless sensor networks
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Summary:Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11101609