Network optimization by regional computing for UAVs' big data

Unmanned Aerial Vehicles (UAVs) are increasingly used in sectors such as surveillance, agriculture, and disaster response, generating massive volumes of real-time big data. Traditional cloud computing introduces high latency, while edge computing suffers from limited scalability. This paper proposes a novel three-layer computing framework incorporating a Regional Computing (RC) layer between UAVs and the cloud. A dynamic offloading strategy is designed to select the optimal computing tier based on network conditions and resource availability. To validate the proposal, we used EdgeCloudSim. Simulation results demonstrate that the RC layer reduces end-to-end processing delays by approximately 80%, lowers operational costs by up to 5× compared to cloud computing, and achieves lower task failure rates relative to edge computing. These findings establish Regional Computing as an efficient and scalable solution bridging the gap between edge and cloud paradigms for UAV big data management.