ZeeFi: Zero-Effort Floor Identification with Deep Learning for Indoor Localization

The knowledge of the floor-level location of a user in a multi-storey building is important for many applications, especially for emergency response. Existing floor identification systems suffer from a variety of limitations such as low accuracy, the need for a time-consuming site survey, assumption of user encounters, knowledge of the initial floor, and/or poor applicability. In this paper, we propose a novel, zero-effort, deep learning-based floor identification system, called \textit{ZeeFi}. The proposed system uses the widely-available smartphone sensing to identify on which floor a user is located. By recognizing the ground floor automatically, the proposed system does not require site survey, initial floor knowledge, and other assumptions. To achieve accurate floor identification performance, we have developed a deep learning-based method. Experimental results show that the proposed system outperforms the state-of-the-art systems, and is very promising for large-scale deployment.