Uncovering mobile infrastructure in developing countries with crowdsourced measurements

Knowledge of cell tower locations enables multiple applications including identifying unserved or poorly served regions. We consider the problem of estimating the locations of cell towers using crowdsourced measurements, which is challenging due to the uncontrolled nature of the sample collection process. Using large-scale crowdsourced datasets from OpenCelliD with ground-truth cell tower locations, we find that none of the several commonly used localization algorithms (e.g., Weighted Centroid) nor the state of the art Filtered Weighted Centroid (FWC) approach that filters out less predictive measurements manage to deliver robust localization performance. We propose a novel supervised machine learning based approach termed as Adaptive Algorithm Selection (AAS) that adaptively selects the localization algorithm likely to provide the most accurate localization performance for a given cell and its crowdsourced samples. We show that AAS not only significantly outperforms the state-of-the-art FWC approach, with median error improvement over 65%, but also achieves localization performance within 20% of an idealized Oracle solution. We validate the applicability of AAS in new and different settings (including WLAN AP localization) before presenting case studies in three different African countries that demonstrate the use of AAS based cell tower localization to reliably infer mobile infrastructure in developing countries.