Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Abstract

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.

Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus

“…Over the last decade, Artificial Intelligence (AI) tools have produced interesting and effective results when solving complex problems that resemble ours, such as automatic system diagnostics and identification [11], fault detection in wireless sensor networks [12], [13], [14], [15], [16], and certain security problems in other fields. Thus, ML techniques, as the most interesting approach in the field of AI, can be very effective for the detection of intrusions.…”

Motion Recognition Based on Sum of the Squared Errors Distribution

“…However, there are some limitations, such as the camera's field of view, the complexity of target background, the intensity of light, and the privacy of the user. With the development of sensor technology, non-visual motion recognition technology has been developed rapidly, such as Hidden Markov Model (HMM) [12], Support Vector Machine (SVM) [13], Principal component analysis (PCA) [14], K-Nearest Neighbor (KNN) [15], Neural Network (NN), Singular value decomposition (SVD), and Wavelet packet transform (WPT). Most of these algorithms use surface electromyography (SEMG) signal acquisition sensor and inertial sensors in the process of data collection.…”

Sensor Fault Diagnosis Method Based on α‐Grey Wolf Optimization‐Support Vector Machine

“…When the fault intensity stays low, there would be some forms of sensor failure showing similar characteristics of data distribution, which is a leading cause for the low levels of diagnostic accuracy [4]. In traditional approaches to fault diagnosis [5][6][7], model-based methods require the establishment of an accurately mathematical model for the research object.…”

“…A very random tree method was proposed to detect and diagnose the faults in sensor networks in [9], which demonstrated strong robustness for processing the signal noise but ignored the fault diagnosis for sensor nodes. In [4], a hybrid continuous density HMMbased ensemble neural networks method is applied to detect and classify sensor node faults.…”