MACHINE LEARNING BASED OUTLIER DETECTION TECHNIQUES FOR IoT DATA ANALYSIS: A COMPREHENSIVE SURVEY

These days, with the popularity and significant advancements of emerging technologies such as Internet of Things (IoT), Cyber-Physical-Systems (CPS), and other wireless sensor technologies, the huge volume of sensor data has generated for IoT devices is vast. In these data, identification and detection of outliers/anomalies is a challenging issue and raised as the primary importance of data analysis. In the olden days, the conventional outlier detection techniques are not effectively applied to deal with outliers over IoT data. Therefore, this paper explores a comprehensive survey of the latest Machine Learning (ML)-based outlier detection techniques for handling outliers in IoT data. Also, surveyed the various smart city based use cases related to IoT applications more significantly. Besides, the required performance evaluation metrics have been addressed for validating the results of ML-based outlier detection techniques. Finally, this article also addressed the possible open research issues that are necessary to deal with outliers in IoT sensor data.