A Secure End-to-End Cloud Computing Solution for Emergency Management via UAVs

To properly determine the severity of an emergency and provide a quick response, information has to be gathered, confirmed, and analyzed. Artificial intelligence assisted UAV can provide a unique perspective and review extensive information for an incident. However, their limited computing capacity and battery duration raise the challenge to compute-intensive mobile applications. In addition, because they serves civilian purposes, the safety and security of UAV communication systems has become critical. To enhance first responders' safety and effectiveness, we developed an end-to-end UAV emergency management system with secure wireless communication links, cloud-based deep learning cognitive algorithms, and easy-to-use mobile interface. We implemented our system onto a hardware platform including Infineon Larix drone, Raspberry Pi and camera, and GPU server. We evaluated the tradeoff between detection accuracy and processing time, and shown that our system achieves end-to-end latency of under 200 ms per frame including data transmission, processing, and reception.