Estimating locations and coverage areas of mobile network cells based on crowdsourced data

Mobile network operators usually keep the data about their mobile network topology very secret. However, having this data, many sophisticated and innovative applications could be realized. Today's smartphones provide the means to measure cell data with which the mobile network topology of different operators can be approximately derived by applying various position estimation algorithms. These approaches deliver different results in terms of distance to the real positions of base stations and positions calculated by Google. In this paper, we analyze and evaluate state-of-the-art approaches for estimating base station positions out of crowdsourced data and integrate the most suitable methode(s) into the OpenMobileNetwork, which is an open-source initiative for publishing mobile network data using the semantic principles of Linked Data. The results show that the grid-based approach provides the best estimates for base station positions when compared with their real locations.