Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
- software failures,
- hardware failures, and
- communication failures.
- Offset fault: Due to a calibration error in a sensing unit, a displacement value is added to the actual sensed data.
- Gain fault: When the change rate of sensed data is different from the expected rate.
- Stuck-at fault: When the variation in sensed data series is zero.
- Out of bounds: When the observed values are out of bounds of expected series.
- Spike fault: When the rate of change of measured time series with the predicted time series is more than the expected changing trend.
- Noise fault: When a randomly-distributed number is added to the expected value.
- Data loss fault: When there are some missing data during a specific time interval in the sensed values.
- Random fault: This is an instant error, where data are perturbed for an observation.
- Supervised learning: Data mining techniques are applied to data with a predetermined label of the classes.
- Unsupervised learning: The techniques are applied to non-labeled data. Data are classified with no previous knowledge.
- Semi-supervised learning: This is a hybrid of supervised and unsupervised learning.
1.2. Challenges and Problem Statement
- There are very limited means and resources at the node level, which compel nodes to use classifiers  because they do not require complex computation.
- The sensor nodes are stationed in dangerous and risky environments, e.g., indoors, war zones, tropical storms, earthquakes, etc.
- The fault detection process  should be precise and rapid to avoid any loss, e.g., the process should recognize the difference between abnormal and normal cases, so that it can contain loss in the case of acquiring erroneous data that could lead to misleading results.
- In this paper, two more faults are induced in the datasets, which are listed below:
- spike fault, and
- data loss fault.
- Following that, six classifiers are applied on the datasets, and an extensive simulation study is conducted in our scenario to detect faults in WSN. The classifiers used are: SVM, RF, SGD, MLP, CNN, and PNN.
- The performance of classifiers is evaluated by four widely-used measures, which are stated below:
- Detection Accuracy (DA),
- True Positive Rate (TPR),
- Matthews Correlation Coefficient (MCC), and
2. Related Work
3. Faults in WSNs
3.1. Gain Fault
3.2. Offset Fault
3.3. Stuck-at Fault
3.4. Spike Fault
3.5. Data Loss Fault
3.6. Out of Bounds
- In the convolutional step, there are three important parts to mention: the input, the feature detector, and the feature map. The main objective of this step is to reduce the size of the input, which would eventually make the process faster and easier.
- The rectified linear unit is the rectifier function (activation function) that increases the non-linearity in CNN.
- Pooling enables CNN to detect features in various images, irrespective of the difference in lighting or their variant angles.
- Once the pooled feature map is obtained, the next step is to flatten it. Flattening means converting the entire pooled feature map image into a single column, which is then fed to the neural network for further processing.
- The last layer is made up of an input layer, a fully-connected layer, and an output layer. The output layer generates the predicted class. The information is passed through the network, and the error of prediction is calculated. The error is then transmitted back to the network for improvement in prediction.
- The first step is to create an RF tree. It is further elaborated in the following five stages:
- K number of random features are selected from total features m, where K is less than m,
- within the selected features, node d is determined using the best split point,
- those nodes are further distributed into daughter nodes through best split,
- the first three steps are repeated until l number of nodes are obtained,
- all of the above steps are repeated for n times to achieve p number of trees, where n is not equal to p.
- The next step is to classify the data based on an RF tree that has been created in the first step. It has the following stages:
- with the rules created for each randomly-formulated decision tree and test features, data are classified,
- the votes are calculated for each target value,
- the highest voted prediction target is considered to be the final result of the RF algorithm.
- Input layer: The first layer is the initiating layer of PNN. It has multiple neurons where each neuron represents a predictor variable. There are N number of groups, and neurons are used as categorical variables. Then, the range is standardized by dividing the subtracted median by the interquartile range. These values are used as an input for the next layer of neurons, which is a pattern layer.
- Pattern layer: This is the second layer of PNN. Each case contains a single neuron in the training dataset. The target value and predictor variables are stored together in a case. Then, a private neuron calculates the Euclidean Distance (ED) from the center point of all neurons. This process is repeated for each and every case. After that, ED is used with the radial basis kernel function along with the sigma values.
- Summation layer: The output of the pattern layer is a pattern neuron that is used as an input for the summation layer. In PNN, each category of target variables has one pattern neuron. After that, every hidden neuron stores the actual target category of each training case; these weighted values are streamed in the pattern neurons that are in compliance with the hidden neuron’s category. These values are added in pattern neurons for the determined class.
- Output layer: The weighted votes are compared in the output layer. The selection criteria for determining which vote should be used for predicting the target category are based on the comparison of weighted votes for each target class that is calculated in the second layer.
5. System Model
5.1. Phase 1
5.2. Phase 2
5.3. Phase 3
6. Simulations and Results
7. Conclusions and Future Work
Conflicts of Interest
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|ANOVA||Analysis Of Variance|
|BBA||Basic Belief Assignment|
|CCAD-SW||Collective Contextual Anomaly Detection using Sliding Window|
|CNN||Convolutional Neural Network|
|CMOS||Complementary Metal Oxide Semiconductor|
|DNN||Deep Neural Network|
|ECOC-SVM||Error-Correcting Output Coding-Support Vector Machine|
|EAD||Ensemble Anomaly Detection|
|FFNN||FeedFoward Neural Network|
|GLDT||Gradient Lifting Decision Tree|
|GLR||Generalized Likelihood Ratio|
|KDE||Kernel Density Estimation|
|LNSM||Log-Normal Shadowing Model|
|MMSE||Minimum Mean Squared Error|
|MSE||Mean Squared Error|
|MCC||Matthews Correlation Coefficients|
|OCSVM||One-Class Support Vector Machine|
|PSO-ANN||Particle Swarm Optimization Artificial Neural Network|
|Probability Distributed Function|
|PNN||Probabilistic Neural Network|
|PDR||Packet Drop Ratio|
|SVM||Support Vector Machine|
|SGD||Stochastic Gradient Descent|
|SVR||Support Vector Regression|
|SHM||Structural Health Monitoring|
|SCADA||Supervisory Control And Data Acquisition|
|TPR||True Positive Rate|
|TA-SSVM||Trend Analysis least Squares Support Vector Machine|
|WSNs||Wireless Sensor Networks|
|XGBoost||Extreme Gradient Boost|
|SVM and Statistical Learning Theory ||Fault detection in WSNs||Upcoming faults are not identified, predicted, and quantified; it does not consider single-hop indoor datasets||Forecasting of upcoming faults|
|OCSVM and SGD ||Anomaly detection in WSNs||No proposed scheme for anomaly identification mechanism||Multi-class, one-class classification problems for efficient anomaly detection|
|LNSM and PSO-ANN ||Measured the RSSI in real environments, determined the distance between sensor and anchor nodes in WSNs, and improved the accuracy of estimated distance||For outdoor environments, the effect of anchor node density on localization accuracy was not calculated||Algorithms can be evaluated through more metrics such as MAPE and MAE|
|SVR and Neighbor Coordination ||Fault detection for WSNs||The proposed algorithm is specific to meteorological elements, and there is no comparison with any other classifier||Comparisons with more than two classifiers for validation|
|CNN ||Image classification and object detection||No mechanism for fault identification||Fault identification classifiers can be incorporated with CNN|
|RF and XGBoost ||To detect fault in wind turbines||The number of features is limited to 10||More comprehensive training data in multiple wind turbine working conditions, including all the unconsidered faults|
|SVM and TA ||Improvised sensor fault diagnosis||Single type of ECOC coding matrix in both feature extraction and fault classification||Parameter optimization processes|
|RF and SVR ||Anomaly detection in energy consumption sensors||No comparison with other hybrid classifiers that are trained on different datasets and features||More robust voting techniques should be explored such as weighted voting|
|Stochastic systems ||Distributed soft fault detection in WSNs||Limitation of the network-induced delay and event-triggered mechanism||Non-linear systems with complex and limited communication|
|PNN ||Heterogeneous fault diagnosis||No variation in the classifier||Further, this methodology can be applied on body area sensor networks, vehicular ad-hoc networks, and UWSN|
|SVM and Statistical Time-Domain Features ||Sensor fault classification||The scheme is implemented on only five faults||More faults should be considered|
|MMSE, Multiple Hypothesis Test, and GLR Test ||Wireless sensor fault detection, identification, and quantification||A large number of time-synchronized samples are required for MMSE identification||Future work in energy efficiency of WSNs|
|DNN and Conventional RF ||Fault detection in a direct drive wind turbine||No evaluation through comparisons||Comparison of DNN with other neural network algorithms|
|GA ||Fault detection in cluster head and cluster members||Limited use of the optimization technique||For better evaluation, multiple optimization techniques can be used to validate the scheme|
|LSVM ||A data-driven framework for reliable link classification of WSNs||Not suitable for unsupervised data||The technique can be enhanced for anomaly identification in link failure|
|SVM, GA, and Tukey Test ||To adjust the transmission rate in WSNs||No scheme for identifying and detecting anomalies in the transmission or congestion rate||Multi-classification to detect faulty nodes|
|Technique||Matthews Correlation Coefficient (MCC)||Rank|
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Noshad, Z.; Javaid, N.; Saba, T.; Wadud, Z.; Saleem, M.Q.; Alzahrani, M.E.; Sheta, O.E. Fault Detection in Wireless Sensor Networks through the Random Forest Classifier. Sensors 2019, 19, 1568. https://doi.org/10.3390/s19071568
Noshad Z, Javaid N, Saba T, Wadud Z, Saleem MQ, Alzahrani ME, Sheta OE. Fault Detection in Wireless Sensor Networks through the Random Forest Classifier. Sensors. 2019; 19(7):1568. https://doi.org/10.3390/s19071568Chicago/Turabian Style
Noshad, Zainib, Nadeem Javaid, Tanzila Saba, Zahid Wadud, Muhammad Qaiser Saleem, Mohammad Eid Alzahrani, and Osama E. Sheta. 2019. "Fault Detection in Wireless Sensor Networks through the Random Forest Classifier" Sensors 19, no. 7: 1568. https://doi.org/10.3390/s19071568