https://github.com/iitis/outdoor_localization_based_on_lora_networks
This study presents a machine learning-based outdoor localization approach utilizing LoRa networks.
https://github.com/iitis/outdoor_localization_based_on_lora_networks
Last synced: 11 months ago
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This study presents a machine learning-based outdoor localization approach utilizing LoRa networks.
- Host: GitHub
- URL: https://github.com/iitis/outdoor_localization_based_on_lora_networks
- Owner: iitis
- Created: 2025-01-29T10:46:39.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-11T13:20:02.000Z (about 1 year ago)
- Last Synced: 2025-04-11T14:44:10.025Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 20.5 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Device Localization Based on RSSI in LoRa Networks
This study presents a machine learning (ML) based device localization approach in an outdoor environment in LoRa networks.
*Dataset*
In this study, we use the latest version of the open-source LoraWAN data set called "Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas."
You can access the data from the link below.
https://zenodo.org/records/3342253
Three Low Power Wide Area Network (LPWAN) datasets, namely "Sigfox_dataset_antrwerp," "Sigfox_dataset_rural," and "lorawan_dataset_antwerp," were recorded from 16 November 2017 to 5 February 2018. In this study, since we focused on outdoor positioning in the LoRa Network, we only used the "lorawan_dataset_antwerp" dataset.
The data set, which contains around $130,000$ in LoRaWAN messages, was collected in Antwerp, Belgium. Each message (each sample) contains $77$ features, including $72$ RSSI values, RX Time, SF, HDOP, Latitude, and Longitude.
# Machine Learning-Based Localization in LoRa
We present the benchmark ML models that we propose for the localization of outdoor devices in RSSI-based LoRaWAN networks: k-NN, CNN, SVR, ANN, XGBoost, and LightGBM.
# Repository Description
* *Data_Description.ipynb* file contains the data preprocessing stage without the normalization script.
* *Enhancing_LoRa_based_Outdoor_Localization_Accuracy_Using_Machine_Learning.ipynb* file is a Supplementary Jupyter Notebook, and it presents the architecture of the ML models.