# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 2 Works on Fault Detection  
[PERSON] et al. have analyzed various random classifiers and tested the result of the classifiers using python. In this approach, the following process is performed as per the order of flow. The data are collected from the sensor nodes, faults are injected randomly on collected data and the faults are detected by all the classifiers used in this approach. The RF classifier outperforms most of the faults in WSN [10]. [PERSON] et al. (2018) have used three techniques to find faults and also to categorize them into different categories. The results of naive Bayes, SVM, and Gradient Lifting Decision Tree (GBDT) are compared and better results are obtained. This work has identified the fault due to low energy on sensor nodes' battery and also detected the short-term fault, noise fault and fixed fault [10]. [PERSON] et al. have implemented the SVM technique and this technique has divided the sensed datasets into two categories: 1 and \(-1\). The datasets are classified into 1 as error-free measurement and \(-1\) as the faulty measurement. This is implemented in the cluster head to classify and for decision making [11]. [PERSON] et al. have deployed SVR mechanisms for fault identification with neighbor coordination. The conditions considered in this technique are lesser node densities and high node failure rates. This technique comes under the prediction model at the sensor node level and also mutual testing by every node in the network [12]. The detected faults have been analyzed in WSN by [PERSON] et al. The faults are divided as (1) transient faults and (2) persistent faults. The faults that occur at sensor nodes due to node failure are permanently classified as Persistent faults. The faults are classified into transient faults due to the sensor node's misbehavior temporarily. An algorithm based on the distribution method for effective handling of streaming of sensed data in WSNs. It is also used for anomalies detection throughout the networks and to find cost functions. They have achieved the lowest misdetection rate and highest fault detection rate [13]. [PERSON] et al. (2018) have analyzed that the link failures are the major reason for the network connectivity issues in WSN. This is handled by them by proposing an analysis technique, namely Feed Forward Neural Network (FFNN), for comprehensive analysis. This FFNN measures the connection link quality to estimate the network lifetime. This technique finds the quality of links by measuring the Packet Drop Ratio (PDR) values in WSN [14; 15]. [PERSON] et al. have effectively used machine learning in WSN to identify faults and for data analysis. The WSNs are more vulnerable to faults and failures due to their nature. The authors have used a data mining technique for fault finding and diagnosis [16]. [PERSON] et al. have identified that the functional cost of WSNs depends on sensor node failures. Hence, it has to be identified and rectified as much as earlier to reduce the operational cost. For fault finding, the RF has been used by the authors. The authors have proposed the technique of data-driven architecture and it has provided better results than SVM [17]. Two techniques for node failure identification and to separate the failure nodes. The technique used for fault finding is Trend Analysis incorporated with least Squares Support Vector Machine (TA-SSVM). To segregate the failed nodes from the WSN, the Error-Correcting Output Coding-Support Vector Machine (ECOC-SVM) technique is used [18]. [PERSON] et al. have proposed a new classifier with a pattern-based approach. The energy consumption pattern in WSN is identified by the Collective Contextual Anomaly Detection using Sliding Window (CCAD-SW) architecture to differentiate the failure sensor nodes from WSN. The authors have also used another technique, namely Ensemble Anomaly Detection (EAD), to classify the sensor nodes [19].