Outlier detection in IoT devices

Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points with few instances. Second, deploy the outlier detection to implement deep learning techniques related to time series analysis. Consequently, we need to use different sensors and boards.