# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 2 Related Work
To solve the problem of wireless emergency communication in maritime emergency communication networks, ref. [22] proposed a wireless emergency communication relay system based on a tethered UAV platform, which accounts for significant advantages in rapid and flexible deployment and long-time spatial coverage. Ref. [23] investigated the deployment of UAVs to optimize coverage quality after a disaster or during episodic events. A decentralized deployment algorithm based on weighted Voronoi cells is proposed, aiming to minimize the average distance between UAVs and users while maintaining the connectivity between UAVs and fixed base stations to improve coverage performance. Although UAVs are playing an increasingly important role in communication coverage, a single UAV coverage solution may not be able to provide stable and long-lasting services in some scenarios due to energy constraints, coverage limitations, and other issues. For this reason, combining UAVs with communications from satellites and traditional base stations to build a synergistic and complementary multilevel communication network will be able to address the limitations in existing research. Much of the literature examines how UAVs can be deployed into existing satellite-ground integrated networks to improve maritime network coverage. For example, considering the impact of the distance between oceanic surface stations and the coastline on the coverage performance, ref. [24] investigates a wide-range maritime communication architecture based on SAGSIN, which analyzes the random distribution of oceanic surface stations on the ocean surface and improves the coverage probability of oceanic surface stations. Ref. [25] proposed a hybrid satellite-UAV-terrestrial network based on NOMA technology with a joint power allocation scheme to maximize the rate and coverage of the offshore network. Ref. [26] combines the advantages of wide coverage of satellites with the high capacity of shore-based systems and deploys UAVs to enhance the coverage of a hybrid satellite-terrestrial offshore communication network, while jointly optimizing the UAV trajectory and flight transmit power. Ref. [27] describes a hybrid satellite-UAV-terrestrial network for maritime communications that achieves extensive coverage and energy efficiency at sea by coordinating different communication links. While these studies consider factors such as energy consumption, communication latency, and coverage, they mostly ignore the computational resource constraints of the UAVs themselves and the dynamic mobility of maritime users. Considering the impact of maritime user mobility on the deployment of communication nodes, prediction algorithms have been introduced to forecast ship trajectories, enabling more effective dynamic deployment. Many studies have focused on trajectory-prediction algorithms for ships. For example, ref. [28] investigated how to predict ship trajectories in the inner harbor of Busan port using AIS data and deep learning techniques, solving the problem of irregular intervals of AIS data through linear interpolation, and improving the accuracy of route prediction in a complex port environment. Ref. [29] proposed a bi-directional data-driven trajectory-prediction method based on AIS spatio-temporal data, constructed an encoder-decoder network driven by forward and reverse integrated historical trajectories, and predicted ship trajectories by fusing the characteristics of the sub-networks. Ref. [30] proposed a multi-gated attention encoder-decoder network that significantly improves the accuracy of ship trajectory prediction. The scheme combines the LSTM network with Gated Recurrent Units and an attention mechanism and enhances the generalization ability and robustness of the model by introducing a soft-threshold residual structure to handle sparse features. Ref. [31] proposes a deep learning-based framework for ship trajectory prediction, consisting of two models: Differential Long Short-Term Memory (DLSTM) and Enhanced DLSTM with Reference Trajectory Correction (Ref-DLSTM).