A Survey on anomaly detection in IoT: Techniques, challenges, and opportunities with the integration of 6G

The combination of Artificial Intelligence (AI) and data science in the Internet of Things (IoT) has enhanced the concept of anomaly detection. It improves system reliability, efficiency, and security in industrial automation, healthcare, smart cities, and cybersecurity sectors. This survey aims to provide an overview of the current developments, issues, and prospects of this growing area of study. This survey also highlights the importance of AI algorithms, particularly Machine Learning (ML) and Deep Learning (DL), in enhancing the capabilities of detection, analysis, and enabling measures in anticipation of future events. In addition, it identifies the major issues considered crucial to the DL application’s general framework, such as the data quality problem, model interpretability, and real-time and scalability issues. It provides potential solutions through federated learning, edge computing, and blockchain technologies. This paper explores how various features of 6G, including ultra-low latency, massive connectivity, and energy-efficient communication frameworks, can significantly enhance the capabilities of IoT anomaly detection systems. Although 5G brings impressive data rates and connectivity, 6G is anticipated to introduce a paradigm shift in real-time analytics, distributed intelligence, and scalability, which is vital for efficient IoT anomaly detection. The emphasis of this paper is on the use case of IoT anomaly detection, given the unique capabilities of 6G that were not fully explored in previous communication technologies, such as 4G and 5G, particularly in terms of handling computational complexity. By addressing many of the previously listed challenges, the ultra-low latency, massive connectivity, and energy-efficient communication frameworks that 6G will support will revolutionize anomaly detection and support processes, resulting in more robust and adaptive IoT ecosystems.