Indoor Localization System

Machine learning solution for Wi-Fi-based indoor positioning and navigation.

The Indoor Localization System is a machine learning-based solution for indoor positioning and navigation, using Wi-Fi fingerprinting techniques to determine location within a building. It addresses the challenge of indoor navigation where GPS is ineffective, providing a robust and accurate method for indoor positioning.

Role and Team Collaboration:

In this project, I played a key role in developing the system's architecture, implementing the machine learning models, and handling data preprocessing. I collaborated with a team of data scientists and software developers to ensure the success of the project.

Outcome:

The system utilizes Wi-Fi signal strength data, coordinates, and access point positions for indoor positioning. It preprocesses this data for training deep learning models, including cleaning and normalization. The system includes deep learning models for building and floor classification, as well as a regression model for predicting precise x, y coordinates within the building.

Challenges and Solutions:

One of the main challenges we faced was dealing with the variability and complexity of Wi-Fi signal data in indoor environments. To address this, we developed sophisticated preprocessing techniques and robust models to handle signal data effectively.

Impact and Applications:

The Indoor Localization System is useful for navigation in large buildings like malls, airports, and hospitals where GPS is ineffective. It can also enhance indoor location-based services and analytics, improving user experience and operational efficiency.

Conclusion and Learnings:

The Indoor Localization System demonstrates the potential of machine learning in solving complex real-world problems like indoor navigation. This project highlighted the importance of data preprocessing and model tuning in handling real-world signal data.

Artifacts Created:

• Repository Link: Indoor Localization System on GitHub