# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
Out of bounds: The range of sensed data varies beyond the range of the sensing circuit. Spike fault: The rate of data sensing and processing of the sensor nodes varies abruptly with the actual data rate of sensing and processing. Noise fault: Random numbers are added with the sensed data. Figure 1: Sensor node architecture. Data loss fault: Due to the sensor fault or communication fault, few data are missed with the actual system data rate. Random fault: Fault occurs instantly without any other specific faults in the system  
Besides the mentioned faults, faults can occur instantly and simultaneously from the sensor and communication units. Monitoring the sensor node's functionality is important to achieve the expected goal of the designed application system. The fault-finding system [4; 5] has to be effective and fast enough to get the required data without any error and delay. Vast amounts of research have been going on at different possibilities with constrained sensor resources. This research is based on multiple mechanisms available in the field of WSN development. In fault-finding mechanisms, three approaches are available. They are the Centralized approach, Distributed approach, and Hybrid approach [6; 7]. These approaches are utilized due to the availability of computing algorithms such as machine learning and deep learning methodologies. Machine learning and deep learning are computerized programs with various possibilities of past behavior [8]. Artificial intelligence plays an important role in machine learning to estimate the outcome based on the past behavior. Data mining is the latest technological term extracted from machine learning and deep learning. It dismantles the data into multiple subsets and plays key support in the process of decision-making as opened by [9]. Machine learning is classified into the following categories based on the available information on given data. * Supervised learning: This approach performs a data mining process with a predefined set of labeled classes;
* Unsupervised learning: This method classifies data into unlabeled datasets without prior knowledge;
* Semi-supervised learning: To achieve the best result, the advantages of supervisor learning and unsupervised learning are combined as a hybrid approach;
* The fault-finding mechanism([PERSON] and [PERSON], 2017) is performed with the following classifiers:
* Support Vector Machine (SVM). * Convolutional Neural Network (CNN). * Multilayer Perceptron (MLP). * Stochastic Gradient Descent (SGD). * Random Forest (RF). * Probabilistic Neural Network (PNN). In the work of the SVM technique, Spatially Organized Distributed Echo State Network (SODESN) and Hidden Markov Models (HMM) are used to find the fault and also for data classification. Instead of the above techniques, have used algorithms to detect the faulty nodes and helped to replace them in time to reduce the risk of network failure in WSN. Fault identification mechanisms are used to detect the fault in sensor nodes and replace them to maintain the connectivity in time. The data sensed and transmitted by these faulty nodes are considered for the evaluation purposes of further fault detection.