Due to a wide range of essential applications, resource limitations, and intermittent irregular responses from cluster sensor nodes, different cluster sensor node faults and failures of distributed wireless sensor networks have occurred in recent years. Due to limited calculation capabilities, limited storage space, resource constraints, and a poor wireless communication environment, relatively few studies have been done on distributed fault diagnosis and fault tolerance in wireless sensor networks, making these networks vulnerable to impending security issues and abnormal detection. This chapter presents a thorough investigation into data quality, with a focus on the features of sensor node information with respect to time and space similarity, as well as recent advances in the study of outlier analysis. In this study, we employ classification to identify outliers by predicting the values of faulty sensor nodes using information gathered from their fault-free neighbors. The proposed method, DSFOD, uses the KNN algorithm to diagnose the proximity of a distance and achieves promising results of greater than 0.99. To accomplish the goal of data outlier detection in WSN, the data are analyzed and detected using the DSFOD method. The simulation’s findings demonstrate the network’s efficiency and superior performance at that specific time.