The Integration of Time Series Anomaly Detection into a Smart Home Environment

Smart home IoT systems have become integral to modern households. To ensure security and safety, prevent hazards, accidents and health emergencies, optimize resource usage, and maintain system reliability, it is essential to have anomaly detection as an integral part of the home management system. Integrating anomaly detection into the smart home environment requires it to be extended to a comprehensive anomaly management process that can be broken down into several stages: data collection and aggregation, anomaly detection, anomaly assessment, decision-making, action-taking, logging and analysis of anomaly events and responses. Our work focuses on three key contributions. First, we explore anomaly detection algorithms to improve detection accuracy, improve classification, and provide users with detailed information on identified anomalies. Second, we present a step-by-step breakdown of the anomaly management process, highlighting how anomaly detection functions as its critical subprocess. Finally, we provide an in-depth explanation of how this management process is seamlessly integrated into a functional smart home environment, ensuring a cohesive and effective approach to anomaly handling.