Indoor Navigation System Using Wi-Fi RSSI

This study is about formulating an indoor navigation framework with the help of Wi-Fi Received Signal Strength Indicator (RSSI) fingerprinting technology which would suit a college campus. GNSS solutions are not found in indoors because there is signal loss and lack of vertical positioning that result from the same. Instead, our solution takes advantage of the pre-existing Wi-Fi infrastructure which means that we have an effective and low-cost indoor positioning system. We collected RSSI data from different Wi-Fi access points placed at various locations to create a fingerprint database that can uniquely identify signal signatures at different spots. This information helps us match real-time data with our database through a KNN algorithm, ensuring accurate user localization and turn-by-turn navigation. The mobile application, developed using Flutter, integrates several libraries including Wi-Fi Scan, Geo Locator, and Path for data collection, processing and visualization while also offering an attractive user interface through Material Design framework for Flutter. This project showcases the potential of Wi- Fi RSSI fingerprinting for indoor navigation, providing a scalable and practical solution for environments with existing Wi-Fi networks.