Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network

Technology evaluation in the electronics field leads to great development of Wireless Sensor Networks (WSN) for a variety applications. The sensor nodes are deployed hazardous environments, and they operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts network lifetime. Hence, must be used wisely prolong that fail due have detect quickly maintain network. In WSN, classifiers faults checking data generated nodes. this paper, six such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest Probabilistic been taken analysis. Six different (Offset fault, Gain Stuck-at Out Bounds, Spike fault Data loss) injected faulty checked classifiers. simulation results show detected more it also outperformed all other category.