A smartphone-based energy efficient and intelligent multi-technology system for localization and movement prediction

Abstract

Nowadays we are assisting to a noticeable proliferation of new generation smart-phones, as well as to the growth of mobile applications development. We are fairly surrounded by a huge number of proactive applications, which automatically provide users with relevant information exactly at the right time and at the right place when they need it. This ability is generally given by the exploitation of context information, and mainly Location Information. However, a location service running onto a smartphone has to deal with a great challenge, which is to manage the trade-off between the service's resources usage and its accuracy. In my work I investigate efficient localization strategies: these include, in addition to the standard location tracking techniques, the support of other technologies already available on mobile phones (i.e., sensors), as well as the integration of either Human Mobility Modelling and Machine Learning techniques. The main purposes of this work are: to reduce the impact that the service has on the device's resources usage in the case of continuous localization; to preserve the privacy of the user by running the whole system on the mobile device without relying on a back-end server; and to offer an ubiquitous coverage.