Deep Reinforcement Learning for Internet of Drones Networks: Issues and Research Directions
Noor Aboueleneen, Abdulmalik Alwarafy, Mohamed Abdallah
Abstract
Internet of Drones (IoD) is one of the promising technologies to enhance the performance of wireless networks. Deploying IoD to assist wireless networks, however, needs to address various design issues. Due to the highly dynamic nature of IoD networks, conventional methods are expected to encounter inadequacies that can be resolved using emerging deep reinforcement learning (DRL) techniques. In this paper, we discuss the application of DRL for addressing various issues in IoD networks. We first overview the main features, types, applications, and services of IoD networks. Then, we briefly discuss some DRL algorithms used to address the issues and challenges of IoD networks. After that, we explain the most crucial issues in IoD networks and discuss some papers that show how DRL can address them. Finally, we provide insights into some promising research directions in the context of using DRL in IoD networks.