# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
###### Abstract
On this basis, there is an urgent need to develop more efficient network architectures and deployment schemes for better performance in massive maritime data transmission and task processing. In the maritime network environment, due to the mobility of ships and the limitation of UAV communication coverage, UAVs need to adjust their flight position and altitude in real time to ensure that they can maintain a stable communication connection with ships. In this case, it is particularly important to accurately predict the ship's route. By predicting the ship's trajectory, UAVs can plan their flight paths and adjust their positions in advance, thus optimizing the deployment and scheduling of UAVs. This not only helps to improve the response speed of UAVs in complex marine environments but also ensures the continuity and stability of communication coverage [11]. Automatic Identification Systems (AISs) can monitor ship dynamics and can obtain real-time information on the transmitted positions of hundreds of thousands of ships around the world [12]. These data can be used to effectively predict the ship's trajectory and provide UAVs with positional information for some time in the future. As a result, the UAV can make real-time adjustments more flexibly, thus significantly improving the response speed and stability of maritime communication signals, and ensuring the UAV's communication coverage capability under complex sea conditions. This paper explores the potential benefits of UAVs in enhancing maritime coverage, with a focus on coordinating UAVs with existing satellites and ground base stations through route prediction to enable dynamic deployment and improve the overall performance of maritime communication networks. The main contributions of this paper are as follows:  
1. We propose a deep learning algorithm-based voyage trajectory-prediction model for predicting the voyage trajectory of the user's vessels, which enables communication nodes to sense the user position more accurately, thereby optimizing the deployment scheme and ultimately achieving dynamic and precise coverage. 2. We designed a RUDD algorithm based on route prediction. The algorithm takes into account the computational resources, communication coverage, latency of the communication nodes, the limitations of the UAVs' battery capacity, and the mobility of the user, aiming to maximize the network coverage, reduce the total latency of the system to process the communication tasks, and reduce the energy consumption of the UAV to enable dynamic deployment. 3. We design simulation experiments to demonstrate that the RUDD algorithm significantly outperforms other basic algorithms in reducing total system cost, improving communication coverage, and reducing system delay. We also test the algorithm under different system model parameters to evaluate its performance. Simulation results show that the proposed algorithm has better stability and confirms its applicability in marine IoT scenarios. The rest of the paper is structured as follows. Section 2 discusses related work. Section 3 describes the system model and problem formulation. Then, Section 4 presents the design of the RUDD program process. In Section 5, the experimental performance is evaluated. Section 6 presents the conclusion.