Anticipatory and Adaptive Communication Framework for Dynamic Flying Ad Hoc Networks using Artificial Intelligence

Drones are becoming an essential part of future robotic systems, especially when many of them need to work together as a team. For these drone teams to operate smoothly, they need a strong and reliable way to communicate with each other. This thesis focuses on building a better communication system for drone networks, helping them share information more effectively—even when flying in dynamic environments without fixed infrastructure. To achieve this, the research introduces a smart, multi-layered framework that improves how drones connect and cooperate using Wi-Fi. It includes new models and protocols powered by advanced techniques, including Artificial Intelligence, to boost reliability before and during flight. The result is a more dependable and efficient drone communication network that can support a wide range of real-world uses—from search and rescue missions to delivery services and environmental monitoring.