A UAV-Assisted Edge Framework for Real-Time Disaster Management

Unmanned aerial vehicles (UAVs) equipped with onboard embedded platforms and camera sensors provide access to difficult-to-reach areas and facilitate in remote sensing and autonomous decision-making capabilities in disaster recovery and management applications. Onboard computations are preferred due to connectivity, privacy, and latency problems. However, edge implementation becomes challenging because of limited onboard hardware resources (in terms of area, power, and storage). In this article, we propose a UAV assisted edge computation framework that compresses the convolutional neural network (CNN) models to be run on an onboard embedded graphics processing unit (GPU) for real-time disaster scenario classification. We use an imbalanced dataset named, Aerial Image Database for Emergency Response (AIDER), to replicate real-world disaster scenarios. Our experimental results show that optimized compressed model's throughput is increased by about 99% which is up to 92x faster than the native model. Furthermore, the model size reduction enabled through the proposed framework is about 84% without compromising accuracy and thus makes it suitable for edge GPUs.