TY - EJOU AU - Ateya, Abdelhamied A. AU - Algarni, Abeer D. AU - Koucheryavy, Andrey AU - Soliman, Naglaa. F. TI - Drone-based AI/IoT Framework for Monitoring, Tracking and Fighting Pandemics T2 - Computers, Materials \& Continua PY - 2022 VL - 71 IS - 3 SN - 1546-2226 AB - Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two main systems; AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing. The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking (SDN). The proposed work is built based on a thermal camera mounted in a face-shield, or on a helmet that can be used by people during pandemics. The detected images, thermal images, are processed by the developed AI algorithm that is built based on the convolutional neural network (CNN). The drone system can be called, by the IoT system connected to the helmet, once infected cases are detected. The drone is used for sterilizing the area that contains multiple infected people. The proposed framework employs a single centralized SDN controller to control the network operations. The developed system is experimentally evaluated, and the results are introduced. Results indicate that the developed framework provides a novel, efficient scheme for monitoring and fighting COVID-19 and other future pandemics. KW - Internet of Things; convolutional neural network; fog computing; software-defined networking; COVID-19; drone DO - 10.32604/cmc.2022.021850