Improving Internet of Things Platform with Anomaly Detection for Environmental Sensor Data

Abstract

Internet of things has an essential role in various application domains. The number of Internet of Things applications makes researchers try to formulate how to design the architecture of the Internet of Things platform so that it can be used generically in various domains. Commonly used architectural designs consist of data collecting, data preprocessing, data analysis, and data visualization. However, sensor data that enters the platform often experiences anomalies such as constant values or being stuck-at zero, which are processed manually at the data preprocessing stage. In this research, we try to design an anomaly detection system on the Internet of Things platform that can automatically improve the platform's performance in detecting anomalies. In this study, we compared the False Positive Rate of several anomaly detection algorithms tested to real datasets in the environmental sensor data application domain. The results showed that the anomaly detector system on the Internet of Things platform had an optimal False Positive Rate of 0.9%.