eXLoc: Understanding Deep Learning-driven Indoor Localization with eXplainable AI

Indoor localization using deep learning has emerged as a promising approach due to its high accuracy in mapping and predicting user locations for complex datasets. However, the inherent complexity of deep learning models often limits their interpretability, creating a gap in user trust and understanding. This paper introduces eXLoc, a novel framework that integrates Explainable AI, Class Activation Mapping (CAM), into deep learning models for indoor localization to enhance model transparency and interpretability. We introduce a new metric called Impact Score to identify significant APs that affect model predictions. This enhances model interpretability and allows a model to identify the influential APs via their impact on localization performance. We have extensively evaluated eXLoc over eight different places from two real-world RSSI datasets. We gained insights into how the model generates predictions, and identified the reasons for the model’s poor performance. These results demonstrate that our approach can be effectively utilized in enabling users to have more trust and understanding of the model in many real-world scenarios.