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Published February 4, 2021 | Version v1
Dataset Open

ProxyFAUG: Proximity-based Fingerprint Augmentation (data)

  • 1. Geneva School of Business Administration, HES-SO


The supplementary data of the paper "ProxyFAUG: Proximity-based Fingerprint Augmentation".

Open access Author’s accepted manuscript version:

Published paper:


The train/validation/test sets used in the paper "ProxyFAUG: Proximity-based Fingerprint Augmentation", after having passed the preprocessing process described in the paper, are made available here. Moreover, the augmentations produced by the proposed ProxyFAUG method are also made available with the files (x_aug_train.csv, y_aug_train.csv). More specifically:

x_train_pre.csv : The features side (x) information of the preprocessed training set.

x_val_pre.csv : The features side (x) information of the preprocessed validation set.

x_test_pre.csv  : The features side (x) information of the preprocessed test set.

x_aug_train.csv : The features side (x) information of the fingerprints generated by ProxyFAUG. 

y_train.csv : The location ground truth information (y) of the training set.

y_val.csv : The location ground truth information (y) of the validation set.

y_test.csv  : The location ground truth information (y) of the test set.

y_aug_train.csv : The location ground truth information (y) of the fingerprints generated by ProxyFAUG. 

Note that in the paper, the original training set (x_train_pre.csv) is used as a baseline, and is compared against the scenario where the concatenation of the original and the generated training sets (concatenation of x_train_pre.csv and x_aug_train.csv) is used. 

The full code implementation related to the paper is available here:



The original full dataset used in this study, is the public dataset sigfox_dataset_antwerp.csv which can be access here:

The above link is related to the publication "Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas", in which the original full dataset was published. The publication is available here:

The credit for the creation of the original full dataset goes to Aernouts, Michiel;  Berkvens, Rafael; Van Vlaenderen, Koen; and  Weyn, Maarten.


The train/validation/test split of the original dataset that is used in this paper, is taken from our previous work "A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning". Using the same  train/validation/test split in different works strengthens the consistency of the comparison of results. All relevant material of that work is listed below:








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