An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology

Abstract

Wireless technologies suitable for Search and Rescue (SaR) operations are becoming crucial for the success of such missions. In avalanche scenarios, the snow depth and the snowpack profile significantly influence the wireless propagation of technologies used to locate victims, such as ARVA (in French: appareil de recherche de victimes d’avalanche) systems. In this work, we explore the potential of LoRa technology under challenging realistic conditions. For the first time, we collect radiopropagation data and the contextual snow profile when the transmitter is buried over a $50\times 50$ m area resembling a typical human-triggered avalanche. Specifically, we detail the methodology adopted to collect data through three test types: cross, maximum distance, and drone flyover. The data are annotated with accurate ground truth which allows evaluating localization algorithms based on the RSSI (received signal strength indicator) and SNR (signal-to-noise ratio) of LoRa units. We conducted tests under various environmental conditions, ranging from dry to wet snowpacks. Our results demonstrate the high quality of the LoRa channel, even when the target is buried at a depth of 1 meter in snow with a high liquid water content. At the same time, we quantify the effects of two main degrading factors for the LoRa propagation: the amount of the snow and the liquid water content existing in the snowpack profiles.

Keywords

• Antenna systems
• ARVA
• localization
• LoRa
• radiowave propagation
• search and rescue