Detecting and Identifying Faulty IoT Devices in Smart Home with Context Extraction

Abstract

A fast and reliable method to detect faulty IoT devices is indispensable in IoT environments. In this paper, we present DICE, an automatic method to detect and identify faulty IoT devices with context extraction. Our system works in two phases. In a precomputation phase, the system precomputes sensor correlation and the transition probability between sensor states known as context. During a real-time phase, the system finds a violation of sensor correlation and transition to detect and identify the faults. In detection, we analyze the sensor data to find any missing or newly reacting IoT devices that are deviating from already grouped correlated sensors, and state transition to find the presence of an abnormal sequence. Then, the system identifies the faulty device by comparing the problematic context with the probable ones. We demonstrate that DICE identifies faulty devices accurately and promptly through the evaluation on various fault types and datasets.