Name
Abstract | ||
|---|---|---|
In this paper, we propose a Deep Neural Network model based on WiFi-fingerprinting to improve the accuracy of zone location in a multi-building, multi-floor indoor environment. The proposed model is presented as a Stacked AutoEncoder (SAE) to allow efficient reduction of the feature space in order to achieve robust and precise classification. The multi-label classification is used to simplify and reduce the complexity of the learning classification task during the training phase. To achieve a hierarchical classification, we applied an argmax function on the multi-label output to convert the multi-label classification into multi-class classification ones to estimate the building, the floor and the zone identifier. Experimental results show that the proposed model achieves an accuracy of 100% for building, 99.66% for floor and 83.47% for zone location with a test time that does not exceed 10.21s. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IWCMC.2019.8766761 | 2019 15TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) |
Keywords | Field | DocType |
Zoning Localization, WiFi-Fingerprint, Deep Neural Network, Stacked AutoEncoder, Multi-Class, Multi-Label | Feature vector,Autoencoder,Identifier,Computer science,Real-time computing,Artificial neural network,Wireless sensor network,Distributed computing,Scalability | Conference |
ISSN | Citations | PageRank |
2376-6492 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Souad BelMannoubi | 1 | 0 | 0.34 |
| Haifa Touati | 2 | 19 | 7.21 |
| Hichem Snoussi | 3 | 509 | 62.19 |