Indoor localization in a WLAN using machine learning and trilateration.
This project was developed as Project Work in Machine Learning (Master in Artificial Intelligence, Alma Mater Studiorum - University of Bologna).
This project work consists of a set of tasks regarding indoor localization, such as:
- Room and floor classification using machine learning methods
- WAPs position inference via trilateration techniques
- WAPs coverage analysis using correlation measures
In particular, since the already available wireless signals are used to profile a location, the indoor localization is based on infrastructure-less approaches. On the contrary, if data were collected using a dedicated network (e.g. BLE), we would have talked about infrastructure-based approaches.
All the code is available in this repository and in this Colab notebook.