# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Single-Layer Satellite Networks
#### 4.1.3 Virtual Topology
DTBR not only maintains high routing capacity under lesser load conditions but relieves congestion in the network and achieves traffic balancing through the cost factor of ISL. In addition, DTBR does not add any additional communication overhead, except for satellites with a large number of failures in the network. The work in [51] integrated the deep-first-search (DFS) and the Dijkstra algorithm for SDN-based massive LEO mobile satellite networks. DFS is a typical path selection approach. However, the performance of DFS depends on the number of nodes in the network. The Dijkstra method decreases the computational overhead by first finding the shortest path between the destination and source nodes during the solution process. Therefore, the scheme takes full advantage of both the DFS and Dijkstra algorithms to improve routing performance. Simulation results show that the joint DFS and Dijkstra algorithms outperform the traditional DFS algorithm. However, the algorithm sacrifices metrics such as end-to-end delay to some extent. For this purpose, a new distributed routing hybrid model was designed in [52]. The scheme utilizes mobile agents (MAs) to collect routing state information. The simulated results show that the model outperforms other routing models in terms of average delay, packet loss rate, and queuing delay. The explosive growth of communication traffic and the uneven distribution of users in LEO satellite networks have led to the emergence of link congestion control protocols. Therefore, the work in [53] suggested a distributed congestion control routing protocol (DCCR) for LEO satellite networks based on flow classification. The solution meets the computing power required by LEO satellite networks and effectively reduces the computing overhead of centralized routing. DCCR classified services by latency and throughput requirements. A distributed routing scheme was applied to route the different services to reduce the load in the network while at the same time ensuring the delay and throughput requirements of the services. Simulation results show that DCCR can better accommodate QoS requirements, reduce queuing time in the network, and maintain low-latency in high-load conditions. Due to the limited resources of the satellite network, the work in [54] designed a virtual network structure that can be updated in real-time to match the topology of the network. The method adopts a virtual topology that allows pre-storing the required topology and routing tables, thus saving a large amount of satellite computing resources with reduced system latency. Since the satellite network is cyclical, the topological changes of the satellite network are also cyclical. In each cycle, we can analyze and calculate the topology of the satellite in advance. The calculated network topology can find the optimal path between any two nodes in the network, reducing the communication overhead of the network, and the routing algorithm is implemented. Through OPNET software modeling and simulation, the algorithm can obtain the average delay between network nodes, network throughput, and other related metrics under various bit rate conditions. Efficient and secure forwarding of packets in the Satellite Internet of Things (S-IoT) is challenging due to the dynamic changes in satellite network topology and node states. Therefore, the work in [55] proposed an S-IoT adaptive routing algorithm based on improved dual-Q learning. The scheme applies reinforcement learning to S-IoT routing policies, enabling them to adapt to dynamic changes in the S-IoT topology and node states. First, the approach sees the whole S-IoT as a reinforcement learning system, treating the satellite and ground nodes as intelligence. Each node needs to maintain two Q-tables for forwarding and evaluation in S-IoT. Second, for the optimal Q value, the proposal utilizes the network congestion level and node state to improve the hybrid Q value, reward value, and discount factor, respectively.