Towards interpretability in fingerprint based indoor positioning: May attention be with us

In a world increasingly pervaded by mobile and IoT devices, position-related information is gaining more and more importance. Highly accurate and standardized positioning techniques are not yet available for indoor scenarios, unlike for the outdoor case. The most commonly used method for indoor positioning is WiFi fingerprinting, which, despite its well-recognized advantages, still suffers from some notable limitations. Recently, approaches relying on deep learning showed promising results even though their lack of interpretability is still a significant drawback. In this paper, for the first time, we propose a domain-specific concept of interpretability, based on identifying the access points that are most relevant to a position estimate. The goal is to enhance the positioning process by gaining novel scientific knowledge and operational insights, without worsening the performance of the task. We show how it is possible to practically achieve both a local and a global notion of interpretability by means of a deep learning model equipped with an attention module, applied to a ranking based fingerprint representation. Since off-the-shelf application of attention does not guarantee to achieve a faithful nor plausible interpretation, we verified through a series of thoroughly designed quantitative and qualitative clustering based experiments the existence of a strong relationship between the obtained interpretations and the positioning domain. Finally, as by-product, we showed an example of how the new knowledge can be used in principle to improve positioning performance.