Device Localization Based on RSSI in LoRa Networks

This study presents a machine learning-based device localization approach in an outdoor environment utilizing LoRa networks and RSSI.

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.

Repository Description

• Data_Description.ipynb file contains the data preprocessing stage without the normalization script.