Cooperative networking and information processing system of earth observation communication UAV under intelligent service background

Unmanned aerial vehicles (UAVs) are employed for smart services through cooperative networking and information processing depending on the demands of the user’s application. Processing heterogeneous information from non-stationary UAVs generates false rates due to service availability and interruptive communications. Considering the above discrepancies, this article introduces a Cooperative Information Processing Method (CIPM) using Distributed Federated Learning (DFL). The proposed method improves the information processing rate based on the connectivity and availability features. Distributed learning focuses on network connectivity and service availability that are adaptable to the UAV displacement. Estimating both factors is provided to leverage service dissemination through wireless communication. The learning paradigm identifies synchronization between the factors considered in confronting the false rate to maximize adaptability. This adaptability is limited in managing UAV displacement and service deliveries across different communication modes. Therefore, the performance is validated using service delivery metrics, processing rate, processing time, connectivity, and availability.