BLE Beacon Based Indoor Positioning System in an Office Building using Machine Learning

Context: Indoor positioning systems have become more widespread over the past decade, mainly due to devices such as Bluetooth Low Energy beacons which are low at cost and work effectively. The context of this thesis is to localize and help people navigate to the office equipment, meeting rooms, etc., in an office environment using machine learning algorithms. This can help the employees to work more effectively and conveniently saving time. Objective: To perform a literature review of various machine learning models in indoor positioning that are suitable for an office environment. Also, to experiment with those selected models and compare the results based on their performance. Android smartphone and BLE beacons have been used to collect RSSI values along with their respective location coordinates for the dataset. Besides, the accuracy of positioning is determined by using state-of-the-art machine learning algorithms to train the dataset. Using performance metrics such as Euclidean distance error, CDF curve of Euclidean distance error, RMSE and MAE to compare results and select the best model for this research. Methods: A Fingerprinting method for indoor positioning is studied and applied for the collection of the RSSI values and (x, y) location coordinates from the fixed beacons. A literature review is performed on various machine learning models appropriate for indoor positioning. The chosen models were experimented and compared based on their performances using performance metrics such as CDF curve, MAE, RSME and Euclidean distance error. Results: The literature study shows that Long Short Term Memory and Multi-layer perceptron, Gradient boosting, XG boosting and Ada boosting is suitable for models for indoor positioning. The experimentation and comparison of these models show that the overall performance of Long short-term memory network was better than multiplayer Perceptron, Gradient boosting, XG boosting and Adaboosting. Conclusions: After analysing the acquired results and taking into account the real-world scenarios to which this thesis is intended, it can be stated that the LSTM network provides the most accurate location estimation using beacons. This system can be monitored in real-time for maintenance and personnel tracking in an office environment