AI empowered data offloading in MEC enabled UAV networks

Mobile Edge Computing (MEC) has become a critical paradigm. It extends traditional cloud com- puting capabilities by moving computation and storage closer to the network edge. A significant challenge in MEC is the inefficiency and high energy consumption in data offloading for mobile edge computing (MEC) systems due to traditional offloading techniques. The problems with these techniques are mainly limited cov- erage, high energy consumption and privacy concerns. The study explores the integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) techniques to enhance data offloading in MEC environments. The primary objective is to utilize AI algorithms that enable UAVs to make intelligent decisions regarding data transmission. These AI-powered UAVs can dynamically adapt to changing network conditions and optimize data transmission processes by considering factors such as network conditions, device capabilities, and energy constraints. The study examines AI-driven strategies for UAV-based data offloading while focusing on how these techniques can improve network efficiency, reduce latency and enhance scalability in MEC systems. This demonstrates the potential of AI-powered unmanned aerial vehicles (UAV) as a viable solution for efficient data offloading in MEC. This research shows that AI-powered UAVs can enhance data offloading in MEC sys- tems by optimizing flight paths, boosting connectivity and improving overall network performance. While AI integration reduces latency and increases scalability, these UAVs offer high connectivity in demanding areas. However, further research is needed to tackle those challenges.