# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Single-Layer Satellite Networks
#### 4.1.2 Virtual Nodes
The solution achieves soft-switching by increasing the number of satellites per orbit. On the improved multi-state virtual network (MSVN) topology, the author suggested a soft-switching algorithm, namely, MSVN-SHO. Experiments have demonstrated that the MSVN-SHO approach has improved performance over the VN-HO in terms of data loss and latency. Therefore, scholars believe that a slight increase in the cost of the system must be taken into account if MSVN-SHO is to be used to its full potential. The work in [37] used a formal model to investigate the dynamics of the LEO satellite network topology and optimized the virtual node model. Compared with the physical topology of the fixed footprint model of satellite networks, the model has the strengths of small snapshot latency and less path variation. Using the small topological snapshot delay to its advantage, the author put forward the dynamic detection routing algorithm (DDRA). The routing solution proposed in this study dramatically enhances the stability and adaptive capability of the network compared to the traditional topological fast photo-routing and distributed routing methods. A VN-based optimal algorithm has a better adaptive capability while hiding the satellite motion characteristics. However, the approach needs to guarantee the system performance when the throughput of the network reaches a high level. Furthermore, in the case of a large number of satellites, employing such a method to find the best path increases the routing calculation overhead of the system. Current routing algorithms for LEO satellite networks are designed based on path distance. Moreover, the path distance factor only considers the propagation delay and ignores the queuing delay. To solve the concern, the work in [38] introduced a low complexity routing algorithm (LCRA) based on load balancing. Based on the location information of the current node and the destination, the algorithm obtained the optimal path following a distributed routing method. The approach eliminates the need for iterative operations and saves a large amount of on-star computing resources. In addition, each node enables the network to dynamically select the next-hop based on the link state by informing the neighboring nodes of their congestion information. Such schemes achieve lower average queuing delays and packet losses. NS2 tests show that the LCRA method has a better end-to-end delay, throughput, and packet loss than the Dijkstra and DRA methods. In addition, the routing algorithm shows better performance in the LEO satellite network, especially in large-scale user networks. The work in [39] presented an LCPR by exploiting the grid structure of the satellite network topology. In contrast, the conventional algorithm selected the path with the lowest propagation delay based on the latitude and longitude of the current and destination nodes. The LCPR algorithm aims to minimize the computational complexity and the probability of packet loss. The algorithm does not need to store the routing table in the satellite, which effectively decreases the space complexity. The whole algorithm has no iterative process, which makes the time complexity reduced to a certain extent. In addition, the algorithm selects the next-hop adaptively according to the probability distribution, which contributes to the balancing of the service load. Compared with [PERSON]'s shortest path (DSP) and DRA algorithms, LCPR exhibits superior performance in terms of combined throughput and packet loss rate. To ensure that packets can reach their destinations efficiently even in the presence of frequent link and satellite node failures, the work in [40] suggested a local repair-based, disruption-resistant, on-demand routing protocol (DODR) for LEO satellite networks. DODR can be considered as an improved ad hoc on-demand distance vector (AODV) routing algorithm [41]. The main objective of DODR is to minimize end-to-end delay and efficiently transmit packets in case of link or node failure.