SoloCell

Abstract

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Despite the pervasive nature of cellular-based solutions, their localization quality depends on the number of cell towers provided by the phone. According to the standard, any cell phone can receive signal strength information from up to seven cell towers. However, the majority of cell phones usually return only the associated cell tower information, significantly limiting the amount of information available to the location determination algorithm. In this paper, we present SoloCell: a novel deep learning-based indoor localization system that utilizes the signal strength history from only the associated cell tower to achieve a fine-grained localization. SoloCell incorporates different modules that lessen the data collection effort and improve the deep model's robustness against noise. Evaluation using different Android phones shows that SoloCell can track the user with a median localization error of 0.95m This accuracy demonstrates the superiority of SoloCell compared to the state-of-the-art systems by at least 210%.