CC BY-NC-ND 4.0Fährmann, DanielDanielFährmannMartín, LauraLauraMartínSánchez, LuisLuisSánchezDamer, NaserNaserDamer2024-05-022024-05-022024https://publica.fraunhofer.de/handle/publica/467072https://doi.org/10.24406/publica-300710.1109/ACCESS.2024.339505110.24406/publica-3007Anomaly detection is a critical task in ensuring the security and safety of infrastructure and individuals in smart environments. This paper provides a comprehensive analysis of recent anomaly detection solutions in data streams supporting smart environments, with a specific focus on multivariate time series anomaly detection in various environments, such as smart home, smart transport, and smart industry. The aim is to offer a thorough overview of the current state-of-the-art in anomaly detection techniques applicable to these environments. This includes an examination of publicly available datasets suitable for developing these techniques. The survey is designed to inform future research and practical applications in the field, serving as a valuable resource for researchers and practitioners. It not only reviews a range of state-of-the-art anomaly detection methods, from statistical and proximity-based to those adopting deep learning-methods but also covers fundamental aspects of anomaly detection. These aspects include the categorization of anomalies, detection scenarios, challenges associated, and evaluation metrics for assessing the techniques’ performance.enBranche: Automotive IndustryBranche: Information TechnologyResearch Line: Computer vision (CV)Research Line: Human computer interaction (HCI)Research Line: Machine learning (ML)LTA: Interactive decision-making support and assistance systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)LTA: Generation, capture, processing, and output of images and 3D modelsSmart citiesAssistant systemsSecurity technologiesSmart environmentsSmart factoriesATHENEjournal article