An experimental dataset using UAVs and LoRa technology in avalanche scenarios

Area of Science:

• Electrical Engineering
• Computer Science
• Geophysics

Background:

• Wireless communication is vital for avalanche Search and Rescue (SaR).
• Traditional beacons (ARTVA) are limited by snow conditions.
• Unmanned Aerial Vehicles (UAVs) offer potential for enhanced SaR communication.

Purpose of the Study:

• To evaluate the feasibility and performance of Long Range (LoRa) technology on UAVs for avalanche SaR.
• To assess LoRa performance under varying snow conditions (dry and wet).
• To provide a dataset for improving UAV-assisted localization in extreme environments.

Main Methods:

• Simulated avalanche scenarios in the Dolomites with buried LoRa transmitters.
• UAVs equipped with LoRa receivers flew various trajectories.
• Recorded Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR).
• Utilized GPS-RTK for precise ground truth positioning.

Main Results:

• LoRa performance was evaluated across different flight paths, durations, and antenna types.
• Data collected on communication metrics (RSSI, SNR) in dry and wet snow.
• Snow characteristics were analyzed for their impact on LoRa signal propagation.

Conclusions:

• LoRa technology on UAVs demonstrates potential for effective avalanche SaR.
• The dataset supports research in UAV-based localization and resilient SaR communication systems.
• Findings encourage innovation in disaster response technologies for mountain safety.