DOI: 10.5061/dryad.w0vt4b939
title: LoRaWAN gateway performance and vehicle tracking data in AERPAW testbed

description: This dataset was collected during an experiment conducted during NC
 State’s Packapalooza festival in August 2024. It evaluates the performance
 of LoRaWAN gateways by analyzing received telemetry data from a moving
 vehicle (i.e., helikite). Key metrics include Received Signal Strength
 Indicator (RSSI), Signal-to-Noise Ratio (SNR), transmission timestamps,
 and geographic data. The dataset supports various research applications
 such as optimizing IoT networks, enhancing data reliability, and
 developing energy-efficient LoRaWAN solutions. The raw and processed data,
 along with SigMF metadata and Python scripts for post-processing, enable
 reproducible analyses and integration into other workflows.
description: The experiment utilized a LoRaWAN-enabled LoStik IoT transmitter
 connected to a LattePanda MiniPC to transmit telemetry data at 1.5-second
 intervals. The transmitted packets contained a combination of timestamps,
 unique package identifiers, and vehicle telemetry. The data was received
 by multiple RAK7289CV2-V1 LoRaWAN gateways deployed at different locations
 (LW1, LW2, LW3, LW4, LW5, CC2, CC3, and RE2), which recorded signal
 strength (RSSI), noise levels (SNR), and reception timestamps.
 To set up and manage the LoRaWAN network, ChirpStack, an
 open-source LoRaWAN Network Server, was used. Uplink payloads were sent
 without requiring confirmation at the MAC layer using the command
 <code>mac tx uncnf &lt;portno&gt;
 &lt;data&gt;</code>. For each transmission, the Data Rate
 (DR) was incremented by one, starting from DR0 and progressing up to DR3,
 enabling an evaluation of the network's performance across varying
 Data Rates. The received data was logged by the
 gateways, capturing additional details such as channel frequency,
 bandwidth, spreading factor, and the gateway's location. This
 comprehensive setup provides insights into both vehicle movement and
 network performance under varying conditions. Python scripts were employed
 for post-processing and analysis, with sample workflows included to
 facilitate reproducibility.
description: # LoRaWAN Gateway Performance and Vehicle Tracking Data
 [https://doi.org/10.5061/dryad.w0vt4b939](https://doi.org/10.5061/dryad.w0vt4b939) ## Description of the data and file structure The dataset contains detailed measurements collected from the transmitter and gateways, categorized as follows: * **Transmitter Information**: * Sent timestamp (sent_timestamp) * Vehicle's geographic location (longitude, latitude, altitude) * Vehicle's orientation (pitch, yaw, roll) * Vehicle's velocity (velocity_x, velocity_y, velocity_z) * Spreading factor (tx_spreadingFactor) * GPS data (number_of_satellites) * Package number and ID * **Gateway Data**: * Received signal strength (rx_rssi) * Signal to noise ratio (rx_snr) * Reception timestamp (rx_time) * The channel used for receiving the data (rx_channel) * RF chain id used for receiving the signal (rx_rfChain) * Gateway geographic location (rx_location_latitude, rx_location_longitude, rx_location_altitude) * Gateway ID (rx_gatewayId) * Timestamp of the gateway (rx_timeSinceGpsEpoch) * **Transmission Details**: * Transmission frequency (tx_frequency) * Data rate and frame counter (tx_bandwidth) * LoRaWAN Data Rate (dr) ### Files and variables #### File: LoRaWAN\_Gateway\_Performance\_and\_Vehicle\_Tracking\_Data.7z **Description:** The dataset is organized into the following files and folders: 1. **CSV Files**: * `failed_tx_packages.csv`: Logs of failed transmissions. * `gateway-dataRate-Table.csv`: Summary of data rates grouped by gateway. 2. **SigMF Files**: * `LoRa_data.sigmf-data`: Raw signal data including SNR and RSSI information formatted according to SigMF v1.2.2 standards. * `LoRa_meta.sigmf-meta`: Metadata information formatted according to SigMF v1.2.2 standards. 3. **Scripts**: * `example_to_convert_sigmf_to_csv.py`: Script to convert SigMF files into CSV format. * `example_to_sigmf.py`: Example script demonstrating how to use the dataset.