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Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization (code)

Authors: Grigorios, Anagnostopoulos; Alexandros, Kalousis;

Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization (code)

Abstract

The code implementation, and supplementary material, of the paper "Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization". Open access link to the paper: https://www.mdpi.com/1424-8220/22/3/1088 ----------------------------------------------------------------------------------------------------------------------------------------------------------------- The following files are provided: DAE_Benchmarking_Public.ipynb : The main Jupyter Notebook file of this work, with all the experiments of the paper. DAE_script.py : The python script with the implementation of the DAE methods and relevant assisting functions. haversine_script.py : Assisting script for geographic distance calculations, based on the Haversine project (https://pypi.org/project/haversine/). Creating_files_LoRaWAN_dataset.ipynb : The Jupyter Notebook used to prepare the data of the LoRaWAN dataset. Creating_files_DSI_dataset.ipynb : The Jupyter Notebook used to prepare the data of the DSI dataset. Creating_files_MAN_dataset.ipynb : The Jupyter Notebook used to prepare the data of the MAN dataset. files.zip : The folder structure containing the data files used. results.zip : The folder structure containing the resulting figures. ----------------------------------------------------------------------------------------------------------------------------------------------------------------- The datasets used are adapted versions of public datasets that were published in the following works: 1) Aernouts, M.; Berkvens, R.; Van Vlaenderen, K.; Weyn, M. Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas. 2019. Available online: https://zenodo.org/record/3904158#.YfNIfOpBxPY 2) Moreira, A.; Silva, I.; Torres-Sospedra, J. The DSI dataset for Wi-Fi fingerprinting using mobile devices. 2020. Available online: https://zenodo.org/record/3778646#.YfNHtOpBxPY 3) King, T.; Kopf, S.; Haenselmann, T.; Lubberger, C.; Effelsberg, W. CRAWDAD Dataset Mannheim/Compass (v. 2008-04-11). 2008. Available online: https://crawdad.org/mannheim/compass/20080411 All credit for the creation of these datasets goes to their authors. We publish here the train/validation/test splits of the processed datasets, used in the current work.

{"references": ["Aernouts, M.; Berkvens, R.; Van Vlaenderen, K.; Weyn, M. Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas. 2019. Available online: https://zenodo.org/record/3904158#.YfNIfOpBxPY", "Moreira, A.; Silva, I.; Torres-Sospedra, J. The DSI dataset for Wi-Fi fingerprinting using mobile devices. 2020. Available online: https://zenodo.org/record/3778646#.YfNHtOpBxPY", "King, T.; Kopf, S.; Haenselmann, T.; Lubberger, C.; Effelsberg, W. CRAWDAD Dataset Mannheim/Compass (v. 2008-04-11). 2008. Available online: https://crawdad.org/mannheim/compass/20080411"]}

Keywords

machine learning, fingerprinting, error estimation, positioning, open code, open data, benchmarking, reproducibility, accuracy estimation, localization

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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