scholarly journals Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1334 ◽  
Author(s):  
Atia Javaid ◽  
Nadeem Javaid ◽  
Zahid Wadud ◽  
Tanzila Saba ◽  
Osama Sheta ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Extreme Learning Machine ◽  
Belief Function ◽  
Decision Fusion ◽  
Wireless Sensor ◽  
Support Vector ◽  
Learning Machine ◽  
Fusion Approach

Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. The decentralized classification fusion problem was the reason to use the belief function-based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques, namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are prone to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. Because of these challenges, efficient fault detection methods must be used to detect faults in a WSN in a timely manner. We have induced four types of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault, and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM gave the first best result for the improvement of the belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN provided the second, third, and fourth best results, respectively. The proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics, i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). Simulations show that the proposed methods outperform the existing techniques and give better results for the belief function and fault detection in WSNs.

scholarly journals An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 198730-198739
Author(s):  
Liang Kuang ◽  
Pei Shi ◽  
Chi Hua ◽  
Beijing Chen ◽  
Hui Zhu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dissolved Oxygen ◽  
Extreme Learning Machine ◽  
Wireless Sensor ◽  
Learning Machine

scholarly journals Fault Detection in Wireless Sensor Networks through the Random Forest Classifier

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1568 ◽  
Author(s):  
Zainib Noshad ◽  
Nadeem Javaid ◽  
Tanzila Saba ◽  
Zahid Wadud ◽  
Muhammad Saleem ◽  
...  
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Random Forest ◽  
Fault Detection ◽  
Stochastic Gradient Descent ◽  
Wireless Sensor ◽  
Support Vector ◽  
Data Loss ◽  
Daunting Task

Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failures such as software, hardware, and communication failures. Due to the sensor’s limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Convolutional Neural Network (CNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Probabilistic Neural Network (PNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. The results are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this paper, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets. Simulations show that the RF algorithm secures a better fault detection rate than the rest of the classifiers.

scholarly journals A Novel Fault Diagnosis Strategy for Heterogeneous Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Li Cao ◽  
Yinggao Yue ◽  
Yong Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Heterogeneous Wireless Sensor Networks ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Fault diagnosis is a guarantee for the reliable operation of heterogeneous wireless sensor networks, and accurate fault prediction can effectively improve the reliability of wireless sensor networks. First, it summarizes the node fault classification and common fault diagnosis methods of heterogeneous wireless sensor networks. After that, taking advantage of the short learning time, fewer parameter settings, and good generalization ability of kernel extreme learning machine (KELM), the collected sample data of the sensor node hardware failure is introduced into the trained kernel extreme learning machine and realizes the fault identification of various hardware modules of the sensor node. Regarding the regularization coefficient C and the kernel parameter s in KELM as the model parameters, it will affect the accuracy of the fault diagnosis model of the kernel extreme learning machine. A method for the sensor nodes fault diagnosis of heterogeneous wireless sensor networks based on kernel extreme learning machine optimized by the improved artificial bee colony algorithm (IABC-KELM) is proposed. The proposed algorithm has stronger ability to solve regression fault diagnosis problems, better generalization performance, and faster calculation speed. The experimental results show that the proposed algorithm improves the accuracy of the hardware fault diagnosis of the sensor nodes and can be better applied to the node hardware fault diagnosis of heterogeneous wireless sensor networks.

scholarly journals FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 28 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Javad Hassannataj Joloudari ◽  
Mohammad GhasemiGol ◽  
Hamid Saadatfar ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Large Scale ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Classification Methods

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.

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