Location prediction and mobility modelling for enhanced localization solution

TLDR: Performing experiments on real users data, it is shown that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if the authors do not account for time features.

Abstract: Current and future mobile applications massively exploit the knowledge of the user’s location to improve the offered services. However, user localization is by far one of the oldest and most difficult issues, due to its dynamism and to unavailability of some technologies in indoor environments. The enhanced localization solution (ELS) proposed in this paper is an innovative self adaptive solution that smartly combines standard location tracking techniques (e.g., GPS, GSM and WiFi localization), newly built-in technologies, as well as human mobility modelling and machine learning techniques. The main purposes of this solution are: to reduce the impact the service has, on the mobile device resources usage (mainly the battery consumption), when it is asked to provide a continuous localization; to help in preserving the privacy of the user, by running the whole system on the mobile device, without relying on a back-end server; and furthermore, to offer an ubiquitous coverage. The aspects mainly explored in this paper are: location prediction and mobility modelling, required to optimally estimate the current location with ELS. We are finding that people tend to move, for most of the time, among a limited set of places and that this can be modelled with a user prediction graph, which is further used to predict the next movement. Performing experiments on real users data, we show that the proposed prediction and the mobility model method of ELS are able to successfully predict the next location, even if we do not account for time features.