Anomaly Detection Using RFID-Based Information Management in an IoT Context

Abstract

IoT-based environments may infer anomalies based on the data processed from their heterogeneous sensors. Within the technologies evolving the IoT concept, nowadays the Radio Frequency Identification (RFID) technology is a de facto standard in areas like retail or logistics. For instance, most retailers attach RFID-labels to their items to avoid stock-out in the inventory or speed up cash processes. Besides identification, RFID provides further RF data which can be used for information management like anomaly detection (i.e. a shoplifting in a RFID loss prevention system). This manuscript presents two IoT scenarios to detect anomalies using multivariate outlier detection methods, uniquely using RFID data. This research empirically evaluates the authors' proposed methods by reproducing a RFID-enabled store, and the two proposed scenarios. The evaluation achieved a False Positive Rate around 0.1% and a True Positive Rate around 87%.