Edge AI for Real-Time UAV Data Processing

The integration of AI with UAVs has enabled a new generation of autonomous aerial systems capable of real-time decision-making and advanced mission execution. Traditional cloud-based processing introduces latency, bandwidth limitations, and security risks, which are unacceptable for time-critical applications such as disaster response, environmental monitoring, industrial inspection, and defense. To address these challenges, this paper investigates the role of Edge AI for real-time UAV data processing, where AI models are deployed directly on embedded platforms such as NVIDIA Jetson, Xilinx FPGAs, and DSP-based accelerators. We present an overview of existing architectures, algorithms, and hardware solutions that support onboard inference, as well as communication frameworks that integrate UAVs into the broader Internet of Things (IoT) ecosystem. Furthermore, we analyze case studies where edge-enabled UAVs have achieved significant performance gains in object detection, predictive maintenance, and autonomous navigation. Challenges including energy efficiency, thermal constraints, and model optimization are also discussed, with a focus on methods such as quantization, pruning, and lightweight deep learning models. Finally, we outline future research directions in federated learning, swarm intelligence, and hybrid edge-cloud architectures to enable scalable and secure UAV operations.

کلیدواژه ها:

Edge AI ، UAVs ، Real-Time Data Processing ، Onboard Inference ، IoT ، Mobile Edge Computing