Conditional Probability-Based Ensemble Learning for Indoor Landmark Localization.

Indoor location awareness enables many location-based services, such as smart homes or smart offices. The huge amount of sensor data collected by nowadays’ smartphones provides a solid basis for applying advanced machine learning algorithms to derive the correlation between indoor locations and sensor measurements. The combination of multiple sensor measurements, such as the Received Signal Strength of surrounding Wi-Fi access points and magnetic fields, is assumed to be unique in many locations, which can be derived to accurately predict smartphones’ indoor locations. In this work, we propose a novel ensemble learning method to provide room level indoor localization in smart buildings. The proposal is based on a conditional probability model, which combines prediction results of multiple individual machine learning predictors using conditional probability concepts to predict class labels. We have implemented the system on Android smartphones and conducted extensive experiments in real-world office-like environments. The experiment results show that the proposed ensemble predictor outperforms individual and ensemble voting-based machine learning algorithms. It achieves the best indoor landmark localization accuracy of nearly 97% in office-like environments. This work provides a coarse-grained indoor room recognition, which can be envisioned as a basis for accurate indoor positioning.