Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network
Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inhere...
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doaj-8c732e14185046e6b90cabc1ecf09e102020-11-25T01:12:28ZengMDPI AGSensors1424-82202020-01-0120374510.3390/s20030745s20030745Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor NetworkMalathy Emperuman0Srimathi Chandrasekaran1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, IndiaSchool of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, IndiaSensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.https://www.mdpi.com/1424-8220/20/3/745wireless sensor networksensor faultsneural networksclassification of faultshidden markov model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Malathy Emperuman Srimathi Chandrasekaran |
spellingShingle |
Malathy Emperuman Srimathi Chandrasekaran Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network Sensors wireless sensor network sensor faults neural networks classification of faults hidden markov model |
author_facet |
Malathy Emperuman Srimathi Chandrasekaran |
author_sort |
Malathy Emperuman |
title |
Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network |
title_short |
Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network |
title_full |
Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network |
title_fullStr |
Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network |
title_full_unstemmed |
Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network |
title_sort |
hybrid continuous density hmm-based ensemble neural networks for sensor fault detection and classification in wireless sensor network |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-01-01 |
description |
Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks. |
topic |
wireless sensor network sensor faults neural networks classification of faults hidden markov model |
url |
https://www.mdpi.com/1424-8220/20/3/745 |
work_keys_str_mv |
AT malathyemperuman hybridcontinuousdensityhmmbasedensembleneuralnetworksforsensorfaultdetectionandclassificationinwirelesssensornetwork AT srimathichandrasekaran hybridcontinuousdensityhmmbasedensembleneuralnetworksforsensorfaultdetectionandclassificationinwirelesssensornetwork |
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