Affiliations: [a] Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| [b] Department of Mathematics, College of Science, Taif University, Taif, Saudi Arabia
| [c] Mathematics Department, Faculty of Science, Sohag University, Sohag, Egypt
| [d] Department of Electronic Engineering, Sudan University of Science and Technology, Sudan
| [e] Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
Correspondence:
[*]
Corresponding author. Sayed Abdel-Khalek, Department of Mathematics, College of Science, Taif University, PO Box 11099, Taif 21944, Saudi Arabia. E-mail: sayedquantum@yahoo.co.uk.
Abstract: Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends.
Keywords: IoUAV, machine learning, deep learning, QoE, network optimization, smart unmanned systems
