Optimizing Task Offloading in Fog-Cloud Computing Environments for Collaborative Unmanned Aerial Vehicles (UAVs)

Abstract

Unmanned aerial vehicles, or UAVs, are employed in a number of tasks, such as emergency response, crowd control, crime prevention, and accident detection. But because UAVs work autonomously, some UAV applications are dynamic and dispersed geographically, necessitating a high degree of real-time computing power. Because of this, processing UAV data locally can be difficult because of their low processing power. Fog and cloud computing can help UAV application development by offering extra resource capacity when needed, hence overcoming such restrictions. It is uncommon in the literature, however, for the construction of complex and effective UAV task offloading algorithms that work with fog and cloud technologies while taking their service latency and energy consumption into account. For this reason, this paper presents a collaborative offloading technique for UAV applications that makes use of the benefits and capabilities of fog and cloud computing. By using this method, UAVs may deliver the necessary resources and services in real time while consuming the least amount of energy and waiting time.