  The rapid development in the field of System of Chip (SoC) technology,
Internet of Things (IoT), cloud computing, and artificial intelligence has
brought more possibilities of improving and solving the current problems. With
data analytics and the use of machine learning/deep learning, it is made
possible to learn the underlying patterns and make decisions based on what was
learned from massive data generated from IoT sensors. When combined with cloud
computing, the whole pipeline can be automated, and free of manual controls and
operations. In this paper, an implementation of an automated data engineering
pipeline for anomaly detection of IoT sensor data is studied and proposed. The
process involves the use of IoT sensors, Raspberry Pis, Amazon Web Services
(AWS) and multiple machine learning techniques with the intent to identify
anomalous cases for the smart home security system.
