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
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