Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down.
Unmanned Aerial Vehicles (UAVs) or Drones have recently emerged as a cost-efficient alternative to address emergency scenarios for multiple reasons. First, UAVs can be rapidly deployed in disaster areas providing on-demand mobile networks. Second, UAVs may rapidly approach difficult-to-reach locations, such as mountains, deserts, or devastated areas and cover large search areas with sparse victims distribution. Finally, given the high penetration rate of mobile devices in our society, it can be reasonably assumed that victims are equipped with smart devices, e.g., smart phones and wearables, that can be detected by UAV mobile networks.
In this work we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area.
Our results, with a prototype implementation in a field-trial, show that SARDO rapidly determines the location of mobile phones (~3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (~5%).
State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through their mobile phones.
