Real-Time Event Detection and Predictive Analytics Using IoT and Deep Learning

Abstract

The internet of things (IoT) has led to an explosive increase in connected devices, generating a massive volume of data. Real-time analytics in IoT systems is crucial for timely decision-making, enhancing system efficiency and reliability. This involves processing discrete IoT data series within a bounded completion time, providing services like data classification, pattern analysis, and tendency prediction. However, the continuous and heterogeneous generation of IoT data poses significant technical challenges. Designing IoT systems to handle this data in a timely manner becomes critical. This chapter comprehensively explores real-time data analytics in IoT systems, elucidating its characteristics and analysing suitable architectures. A survey of existing applications highlights system design perspectives and performance shortcomings. Lastly, challenges in applying real-time analytics in IoT systems are identified, paving the way for future research directions.