Control of Software-Defined Networks of Unmanned Aerial Vehicles using Distributed Deep Learning

Abstract

There are a variety of civilian, public, and military applications that might be developed for drones. Because they come equipped with their own communications infrastructure, they may be remotely controlled from a distance. Unmanned Aerial Vehicles (UAVs) are gaining popularity for its utilization in a range of activities due to their low cost, versatility, ease of deployment, and the ability to replace manually-operated aircraft in many situations. These vehicles are capable of performing a wide range of activities, such as monitoring, managing crowds, providing wireless coverage, and surveillance. Unmanned Aerial Vehicles (UAVs), often known as drones have the ability to offer solutions that are not only trustworthy but also economical for addressing a wide range of real-time challenges. With the inherent characteristics such as mobility, flexibility, and compatibility in terms of communications, UAVs are able to provide a wide range of services. The ability to monitor a particular area and the flexibility to react to changing demands for services proves the effectiveness of deploying Unmanned Aerial Vehicles (UAVs). As a result, deep learning, also known as DL, is utilized in an increasingly broad manner to overcome the challenges that UAVs face in terms of connectivity and resource utilization.