Challenges and Innovations in Routing for Flying Ad Hoc Networks: A Survey of Current Protocols

Abstract

The proposed study provides a comprehensive overview of the unique challenges faced by Flying Ad Hoc Networks (FANETs), which utilize Unmanned Aerial Vehicles (UAVs) as dynamic nodes in three-dimensional space. The inherent high mobility of UAVs leads to rapidly changing network topologies, frequent link failures, and difficulties in maintaining reliable communication. Traditional routing protocols often struggle to adapt to these conditions, necessitating the exploration of advanced solutions. This survey emphasizes the potential of machine learning, particularly reinforcement learning, to optimize routing paths in such dynamic environments. By categorizing recent developments in reinforcement learning-based routing protocols for FANETs, the paper identifies areas for improvement and ongoing challenges, aiming to guide future research and innovation in enhancing aerial communication systems. Ultimately, the findings contribute to the advancement of intelligent routing solutions tailored to the complexities of FANETs, with implications for applications in disaster response, military operations, and environmental monitoring.