Edge-based Anomaly Data Detection Approach for Wireless Sensor Network-based Internet of Things

Abstract

Nowadays, Internet of Things (IoT) has been widely employed in different applications, such as health care, manufacturing, and weather forecasting. However, due to sensor sensitivities, potential harsh environmental interference, and deception, IoT data is normally apt to be imperfect and erroneous. This paper presents an edge-based approach based on the Gaussian mixture model and fuzzy measure to detect anomalous data without prior knowledge or training to overcome such adverse issues. The experimental results demonstrate that the proposed approach is efficient and effective in detecting anomaly data and achieves detection accuracy ranging from 93% to 100%.