Joint UAV 3D Trajectory and Resource Allocation for Integrated LEO Satellite and Multi-UAV-Enabled Marine IoT Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

The Marine Internet of Things (MIoT) has experienced widespread adoption in the maritime industry; however, its communication infrastructure faces significant challenges due to a lack of terrestrial networks and the dynamic nature of oceanic environments. In response, low-Earth orbit (LEO) satellites have been suggested as an alternative solution. However, LEO satellites also face limitations, including coverage gaps due to the power constraints of MIoT devices and the curvature of the Earth, which impede continuous connectivity. To address these challenges, integrating unmanned aerial vehicles (UAVs) as aerial base stations between LEO satellites and MIoT devices has gained attention as an effective solution to enhance communication. Managing UAV mobility and ensuring reliable communication requires autonomous 3D trajectory optimization and dynamic resource allocation. This paper presents an integrated LEO and multi-UAV-enabled marine IoT network designed to maximize communication coverage, system throughput, and fairness. To achieve this, we formulate a joint UAV trajectory and resource allocation (UTCRA) optimization problem. To address the UTCRA problem, we propose a two-stage learning approach: 1) Gaussian mixture models (GMM)-based clustering is initially applied to dynamically cluster MIoT devices under different UAVs, ensuring a balanced distribution of user loads among UAVs. 2) A federated multi-agent deep deterministic policy gradient (FL-MADDPG) algorithm is employed to continuously optimize UAV trajectories and resource allocation, thereby enhancing communication efficiency. Simulation results demonstrate that FL-MADDPG with GMM significantly enhances communication coverage, average throughput, and fairness. It outperforms benchmark algorithms across all evaluated metrics, highlighting its strong potential as a method for next-generation marine IoT networks.