# Dynamic Routings in Satellite Networks: An Overview
## 5 Potential Technologies and Future Directions
### Potential Technologies  
According to the current research on multi-layer satellite networks and the results of existing works on dynamic routing mentioned, some potential technologies and directions for future dynamic routings in satellite networks and space-air-ground integrated network are provided. **Machine Learning (ML):** The dynamic network topology increases the complexity of satellite networks, and link switches in satellite networks exacerbate routing design problems. In recent years, academics have tried to use machine learning to optimize satellite routing [111]. Artificial intelligence (AI) has a strong learning ability and good generalization, making an intelligent network layer possible. In contrast to traditional mathematical model-driven distributed routing algorithms, machine-learning-based routing algorithms are usually data-driven, allowing them to adapt better to changing satellite networks and optimize network performance metrics dynamically. By using deep learning, significant progress has been made in regulating congestion at the transport layer [112], detecting network security [113], and optimizing video stream transmission [114]. In particular, Figure 10 shows common deep-reinforcement-learning-based routing solution models for traffic prediction routing optimization problems [115]. Handling routing tables generated by different layers of satellite nodes in a satellite network is an important challenge in the satellite routing optimization problem. To alleviate the computational overhead of processing the data, ML can be considered to improve the data efficiency of the routing training process, especially for multilayer satellite networks. To further exploit the topology information, the work in [116] designed a distributed intelligent routing algorithm based on GRU and GNN. The trained GNN model is able to achieve 98% accuracy within 15 iterations relative to shortest-path routing. However, the high input/output feature dimensionality can be formulated via objective function variables, and the satellite routing optimization problem can be formulated via objective functions. The satellite routing optimization problems are generally complicated to solve via traditional methods. Machine learning methods seem to have a much better performance in solving this kind of optimization problem, according to the existing works in the academic literature. **Mobile Edge Computing:** Edge computing enables these resources at the edge of the network to meet critical industry needs for agile connectivity, real-time services, data optimization, application intelligence, security, and privacy [117]. New service models such as mobile edge computing (MEC) and fog computing architectures break the obsolete and limited framework of the cloud computing system [118]. They bring computing and storage resources closer to the devices. So, the high computing agility and low latency can be achieved [119; 120]. Compared to traditional cloud computing models, edge computing models have the merits of real-time data processing and analysis, high security, privacy protection, scalability, location awareness, and low traffic. Dynamic topology policies send data packets between satellite nodes and use real-time routing information to choose the best routes in satellite networks. However, the dynamic routing mechanism requires a high level of data processing due to the satellite's motion. There is no doubt that edge computing technology with powerful data processing and  
Figure 10: Deep-learning-based routing model. analysis can solve this problem. Mobile devices can collect and analyze vast amounts of data, crucial for satellite operations. Implementing the deployment of edge servers near mobile devices can enhance data analytics through high network bandwidth and low latency.On-board processing technology enables multi-layer satellite networks with more powerful communication abilities, equipped with the MEC server [121; 122; 123].