Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMARTCOMP.2018.00078 | 2018 IEEE International Conference on Smart Computing (SMARTCOMP) |
Keywords | Field | DocType |
Indoor Localization,Machine Learning,Deep Learning,TensorFlow,Smart Environments | Smart environment,Wireless,Computer science,Fingerprint,Human–computer interaction,Global Positioning System,Artificial intelligence,Deep learning,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-4706-6 | 0 | 0.34 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabrizio De Vita | 1 | 6 | 4.36 |
| Dario Bruneo | 2 | 362 | 37.34 |