IoT sensors are widely spread and collecting enormous amount of data. However, sensor data may contact noises caused by different factors, which will impact the data processing steps later on for providing intelligent services. This project aims to explore the existence and the type of anomalies (i.e. abnormal data) in IoT sensor time series, and investigate the potential to identify and classify them.
Undergraduate
The students will
1. Explore and understand different types of sensor data anomalies.
2. Investigate anomaly detection methods and find an IoT time series scenario.
3. Prepare IoT time series datasets and condcut anomaly detection.
None
No lab has been assigned to this project