Leveraging Blockchain and Deep Learning for UAV-assisted Search and Rescue Missions

Unmanned aerial vehicle (UAV) networks are a recent development in the rapidly expanding field of wireless communications. With the increasing popularity of UAVs, they are used in different domains, including logistics, surveillance, and search and rescue (SAR) operations. This paper tackles the problems of data security, transparency, and energy efficiency in SAR missions. There are a few works in the literature that provide UAV-based SAR solutions. However, these works do not tackle all three problems at once, which deters a comprehensive and effective approach to utilizing UAVs for SAR missions. In this work, the integration of blockchain technology is proposed to enhance data security, resource allocation, and coordination and ultimately contribute to more efficient UAV-assisted SAR operations. In addition, this work implements a lightweight YOLOv5n model, trained on the VictimDet dataset, which consists of images of disaster-stricken casualties. The results demonstrate that the proposed approach provides data security, energy efficiency, and high casualty detection accuracy, making it a comprehensive framework for UAV-based SAR missions.