A deep learning approach for indoor user localization in smart environments

Nowadays, smart environments are becoming an integral part of our everyday lives. Objects are becoming smarter and the number of applications where they are involved increases day by day. In such a context, indoor localization is a key aspect for the development of smart services which are strictly related to the user position inside an environment. In this paper, we present a deep learning approach to estimate the indoor user location starting from its Wi-Fi fingerprint composed by those signals perceived in the environment. We show some experimental results that demonstrate the feasibility of the proposed approach