# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 2 Related Work
The best set of wavelets is identified by the values of the signal-to-noise ratio and Mean Squared Error (MSE). The internal voting process of RF classifiers is then used for selecting the five dimensionless features as an input. This method was tested on practical bearing vibration signals by taking into account several fault classes. The results produced showed a record of high classification accuracy with increased efficiency and robustness in the model. WSNs can be used in almost every domain and field now. In [18], the scenario was based on wind turbines and incipient fault detection in wind turbines. It was carried out through Deep Neural Network (DNN). Further, four steps were discussed involving a preprocessing method that considers the operational mechanism. This step is used to get rid of outliers in Supervisory Control And Data Acquisition (SCADA). RF is also used to determine the importance of variables associated with target variables. Then, SCADA data are used for training DNN, and the fault threshold is determined by the exponentially-weighted average control chart. The metric that is used to evaluate the model of DNN is the testing error of the wind turbine. This approach is found to be successful, and it was deployed in a direct drive wind turbine generator. Link failure is one of the key issues in WSNs [19]. This occurs because of unidentified faults, which have random instances in a network. This also increases the number of faulty nodes and traffic overhead. To overcome such a problem, a cluster that is based on a fault-tolerant technique using the Genetic Algorithm (GA) is proposed. A number of backup nodes are selected for each cluster head through GA. It is based on sponsored coverage and residual energy parameters. The results showedthat the proposed technique minimizes the energy loss and overhead by detecting faults that occur in cluster heads and members. In [20], an approach was suggested for automatic crack detection and classification in subway tunnel safety monitoring. The process was described in three phases: The first phase consists of storing the digital images of the tunnel surface through industrial cameras. In the second phase, by utilizing morphological image processing techniques and threshold operations, the dark areas with potential crack defects are separated from the grayscale stored images. The third phase is a feature extraction process; a distance histogram is used for describing the difference between cracks and other peripheral objects. To address the challenges of WSN and the renowned problem of the decentralized multi-class classification fusion problem, a simple, but effective decision fusion rule based on belief function theory was proposed in [21]. Based on the classifier's training output confusion matrix and real-time observations, Basic Belief Assignments (BBAs) are constructed for each sensor. Furthermore, [PERSON]'s combinational rule is used to formulate global BBA in the fusion center, which has simplified decision making operations. The experimental results showed that the proposed fusion rule performs better in terms of fusion accuracy for WSNs. In [22], the authors addressed the problem of distributed sensors' failure detection in networks through a novel technique based on a gossip algorithm and group testing principles. Likewise, in [23], a distributed fault detection algorithm was proposed for WSNs. Faulty sensor nodes are identified based on comparisons between the neighboring node and dissemination of the decision made at each node. The authors of [24] proposed another distributed fault detection algorithm. In the proposed technique, a node first collects the measurements of its neighborhood and processes them to decide whether they contain outliers or not, and then, the results are broadcast. Similarly, in [25], the authors proposed and analyzed the performance of two distributed algorithms to help each node in determining whether it is equipped with a defective sensor or not.