# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 2 Related Work
The first step includes Trend Analysis incorporated with least Squares Support Vector machine (TA-SSVM). This algorithm is used for discovering faults in the sensors. The second step involves an improvised Error-Correcting Output Coding-Support Vector Machine (ECOC-SVM). It is used for identifying faults to separate failure nodes of a sensor distinctly. A new pattern-based anomaly classifier was proposed in [12]. The Collective Contextual Anomaly Detection using Sliding Window (CCAD-SW) architecture distinguishes abnormal energy consumption patterns by overlapping SWs of smart buildings. To improve the potential of CCAD-SW, another framework was proposed called Ensemble Anomaly Detection (EAD). It elects classifiers on the basis of majority voting, and it is deployed by combining pattern-based and prediction-based anomaly classifiers to incorporate diversity among classifiers. For the problem of a nonlinear stochastic system with WSNs, a distributed filtering technique was presented in [13]. To characterize the randomly-occurring packet losses, the Bernoulli distribution is used between WSNs and filter units. In addition to that, a fault reference model is used for enhancing the performance of the fault detection system. The Lyapunov functional approach is used to concentrate on the fault detection system's performance and stability. Their simulation results show the effectiveness and applicability of the presented interval type-2 fuzzy model-based stochastic systems with WSNs. In [14], the authors proposed a protocol for heterogeneous faults present in WSNs. The scheme is divided into three phases. The types of fault include hard permanent, soft permanent, intermittent, and transient faults. Packet drop and end-to-end delay are decreased through a time division-based multi-channel MAC protocol. Energy consumption in WSN is decreased through a load balancing clustering method. The second phase of fault detection of hard permanent faulty nodes is determined by a time-out status register mechanism. Analysis Of Variance (ANOVA), a statistical mechanism, is used to test differences between two or more means. In [15], the authors considered five types of fault in a dataset, which was collected through an Arduino UNO micro-controller board. A total of 100 samples were prepared, and each sample contained 1000 data elements. SVM and neural network are used for classification, and all three kernel functions are used to train the SVM, which includes linear, polynomial, and radial basis kernel functions. A cross-validation method is adopted to overcome the issue of overfitting of the model. The results indicated that an increase in the data sample enhances the efficiency of the model, while an increase in the training sample up to 400 reduces the capability of SVM to classify stuck-at fault. Similarly, the authors discussed detection, identification, and quantification of the sensor faults in a network. The scenario they took on is of a Structural Health Monitoring (SHM) and control system through the sensor. Seven different fault types were investigated in [16] and also modeled: bias, gain, drifting, precision degradation, complete failure, noise, and constant with noise. Minimum Mean Squared Error (MMSE) is used for the estimation of each sensor. Furthermore, a sensor fault is identified and quantified by using multiple hypothesis tests using the Generalized Likelihood Ratio (GLR). The sensor network is modeled as a Gaussian process, and it is experimentally evaluated with an array of the accelerator group together on a wooden bridge. A new hybrid approach of RFs classifier was proposed in [17] for detecting faults in rolling bearings. A rolling bearing is a bearing having loads between two races. It is used for reducing rotational friction and supports radial and axial loads. A fault in rolling bearings could result in decaying of the rotational machine's operating conditions and capabilities. Wavelet packet decomposition is used for extracting parameters of fault features.