# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 2 Related Work  
In the context of WSNs, there are many issues that complicate the task of fault detection at the node level. Some of these issues include; energy and resource limitations, environmental constraints, and non-stationary data. A sensor should be able to differentiate between a gain fault and a fire. This diagnosis should be efficient to restrict the loss. To confront this problem, the classification method was used. In [1], the authors have proposed a technique of classification through SVM. It divides the data into two classes: one for normal measurement and \(-1\) for faulty measurement. The decision function is then deployed in the cluster head to classify the data. Likewise, in [6], the authors proposed a mechanism for fault detection based on Support Vector Regression (SVR) and neighbor coordination. They tackled a similar issue of low node densities and high failure ratios. SVR is used to develop a prediction model, whereas node status is identified through mutual testing among reliable neighboring nodes. An analysis of fault detection strategies in WSN was done in [2]. The authors divided the faults into two categories: (1) persistent faults and (2) transient faults. Persistent faults are permanent faults, whereas transient faults are temporary faults. Then, to consider the data-centric perspective, they also categorized soft faults into various categories: offset, gain, stuck-at, out of bounds, spike, and data loss faults. In [3], an online distributed method was proposed to handle the streaming data in WSNs. The approach was based on One-Class Support Vector Machine (OCSVM) to detect anomalies over networks and to get a decentralized cost function. Instead of kernel functions, they used a random approximate function. For approximate dimensions, a sparse constraint is also added in the decentralized cost function. After that, SGD is used to minimize the cost functions and derive two distributed sets of rules. This algorithm contributed to achieving high true positive and low misdetection rates. To determine the distance between the sensor and anchor node in WSNs, the authors in [4] proposed two algorithms. The first method estimated the distance according to the Log-Normal Shadowing Model (LNSM), while the second method used is based on Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) to increase the distance estimation accuracy of the mobile node. In [5], the authors worked on detection of link failure in WSN. The challenge is to prevent link node failures, which might end up in a disjoint set of nodes in the network. This problem leads to rapid reduction in communication. To tackle this issue, they used a FeedForward Neural Network (FFNN) with comprehensive theoretical analysis. The FFNNN model learns the possible distribution of the quality of the links, and it is able to detect it once supplied to the series of Packet Drop Ratio (PDR) values. Similarly, the authors compared SVM, naive Bayes, and Gradient Lifting Decision Tree (GBDT) to identify and classify faults in [7]. Their main focus was on noise fault, short-term fault, and fixed fault, which are caused by low battery and calibration. Whereas, in [8], machine learning techniques were discussed in the context of dynamic behavior of data. In [9], a technique was proposed for object detection using CNN. The authors highlighted the importance of machine learning in WSN applications. WSNs are prone to faults and failures because of the multiple reasons discussed above. Data mining techniques like RF and Extreme Gradient Boost (XGBoost) are used to diagnose faults and failures in wind turbines [10]. Since the operational cost depend on component failures, it is essential to discover errors as soon as possible. RF is used to determine feature importance in the framework proposed, while XGBoost trains the classifier for each and every fault. The proposed data-driven architecture gives better results as compared to SVM, considering multidimensional data. In [11], the authors proposed a method involving two steps.