ABSTRACT
Internet of Drones (IoDs) are the emerging form of futuristic IOT devices where unmanned aerial vehicles have a complete network connection capability using internet. Nowadays, IoDs have been widely used in different application areas such as military missions, civilian purposes, disaster management, traffic surveillance and many more. These IoDs use different approaches to accomplish targets by following some paths using its sensors’ range. With the proliferation of IODs in different operations, improvement is required in the functionality related to autonomy and remote sensing capability for the purpose of capturing robust real time obstacle detection and avoidance system. But IoDs still suffer from many problems such as the drainage of energy, building a communication link, automating the task of detection, management of traffic, maintaining quality of services, etc. To overcome these problems, machine learning (ML) and deep learning (DL) algorithms have proved to provide promising alternative for enhancing the functionality of IoDs in the real-world realms. This chapter reviews the most recent development of IoDs based on ML and DL approaches. It provides the detailed explanation about different techniques of ML and DL used for improving the model of IoDs with the aspect of navigation, battery scheduling, object tracking and collision avoidance and security. The impacts of ML and DL algorithms on the operations performed by IoDs are also analyzed. It includes various challenges and gaps present in the current scenario, to allow future researchers to develop solutions for designing new generation Internet of Drones.
