Anomaly Detection and Complex Event Processing over IoT Data Streams

This book looks at how streams of data from Internet of Things devices can be processed, and how anomalies can be detected within them. It also covers complex event processing. It presents newer, more general methods for handling these streams and for adding contextual meaning to the data at the edge, in the fog, and in the cloud.

The book is organized around foundational models, concepts, and algorithms, followed by system architectures and working technical solutions, with eHealth as the main application area. A central case study follows biometric signals. Raw ECG readings from sensors are processed dynamically along the data pipeline and classified using Hierarchical Temporal Memory and deep learning methods.

The adaptive solutions described are designed to carry over to other eHealth use cases. They support a fuller analysis of patient data, covering past trends, future predictions, and recommended actions.

The final chapter discusses ethical questions, emerging research directions, and the open problems and challenges that remain in IoT data stream processing.