@inproceedings{8a1e3579eb1246ceaf9538962dc057b8,
title = "A Convolutional Neural Network Architecture for Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting",
abstract = "Nowadays Location Based Services applications are increasingly useful. However, problems like floor identification for multi-buildings and adverse effects of devices diversity are needed to be resolved. In this paper we propose a new approach using cosine similarity computed by Wi-Fi fingerprints and radio map and using Convolutional Neural Network (CNN) model to achieve multi-floor classification. We propose in this paper to use locations-based similarity as the feature vector instead of using conventional Access Point sets. We also use a timesaving walk-survey method to collect Wi-Fi fingerprint. Experimental results show that our proposed CNN floor classifier has 98.37% training accuracy and 99.51% test accuracy. Compared with recent deep neural networks, our proposed approach achieves state-of-the-art floor classification accuracy but only needs a training data set almost 5 times smaller than that of other approaches.",
keywords = "CNN, Cosine similarity, floor classification, small data set",
author = "Xin Chen and Siu, {Wan Chi} and Chan, {Yuk Hee} and Chan, {Chuen Yu} and Chau, {Chun Pong}",
note = "Funding Information: This work is partly supported by CIHE (ISG200206) and HK UGC Grant (UGC/IDS(C)11/E01/20), and the Wi-Fi data and indoor digital map in this work was supported by the Maphive Technology Limited company. Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th International Conference on Digital Signal Processing, DSP 2023 ; Conference date: 11-06-2023 Through 13-06-2023",
year = "2023",
month = jun,
doi = "10.1109/DSP58604.2023.10167952",
language = "English",
series = "International Conference on Digital Signal Processing, DSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2023 24th International Conference on Digital Signal Processing, DSP 2023",
}