Trustworthy Dynamic Data Awareness Model for Tracking in CPS

Abstract

With the development of Internet of Things (IoT), the interconnected devices and sensors in cyber-physical systems (CPS) are increasing; these continuously exchange collected data for revealing helpful information about the overall system. In CPS-based monitoring applications, abnormal (including anomalies/outliers) values can lead to severe consequences for skewed judgments. The criteria for determining abnormal values may change over time, making it impossible to detect abnormal in real-time based on a training model or rely on traditional statistical methods to find abnormal values efficiently. When machine learning using, abnormal values in the data set are considered data errors or noise and excluded from analysis for the stability of the results. However, the identified abnormal values contain essential information in some cases, making correct navigation and identifying anomalies even more critical. This paper proposes a Trustworthy Dynamic Data Awareness (TDD-Awareness) algorithm that extracts the characteristics of continuous sensor data and accurately identifies abnormal values through the subsequent preprocessing process. The TDD-Awareness algorithm extracts the number of generated abnormally, the time of occurrence, and the characteristics and patterns needed to analyze the location of occurrence from the sensor data. The importance of "abnormal values" is determined by effectively exploring the relationship between abnormally to separate containing necessary information.