Published February 4, 2021 | Version v1
Software Open

ProxyFAUG: Proximity-based Fingerprint Augmentation (code)

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


The code implementation of the paper "ProxyFAUG: Proximity-based Fingerprint Augmentation".

Open access Author’s accepted manuscript version:

Published paper:


More specifically: : This python script contains all necessary methods implementing the ProxyFAUG augmentation scheme.

Augmentation_test.ipynb : This notebook includes all tests of the paper. : Assisting script for geographic distance calculations, based on the Haversine project (

environment.yml : The .yml file from which the environment used for the tests of this study can be recreated. It includes all requirements, in terms of packages used and their exact versions.


The datasets produced by this code have been made available here:



As mentioned in the Augmentation_test.ipynb notebook, and in the paper, this work uses the train/validation/test sets of the 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:






The train/validation/test sets used in this study were created from the original full 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: credit for the creation of the original full dataset goes to Aernouts, Michiel;  Berkvens, Rafael; Van Vlaenderen, Koen; and  Weyn, Maarten.



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