# 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  
With the virtual topology strategy, the operational period of the satellite is divided into n time intervals. The satellite network topology remains unchanged between time gaps, so routing optimization can be performed using terrestrial network protocols. The virtual-topology-based routing algorithm only needs to calculate the route at the current time. This approach uses visible satellites to build a visible matrix within each time slice and calculates the optimal route based on traffic demand. The ultimate goal is to fully utilize the limited satellite link resources. However, a link allocation algorithm generally adopts a centralized calculation approach and cannot sense network congestion. Table 6 summarizes the works related to the virtual topology routing algorithms. Dynamic virtual topology routing (DV-DVTR) was introduced in [44]. First, a virtual-topology-based connection-oriented routing algorithm based on ATM was proposed. DVTR is a system period of the satellite network divided into time slices. In each time slice, the satellite network topology is considered to be fixed. The V-DVTR algorithm \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Proposed Algorithm/Scheme** & **Reference** & **Features/Advantages** & **Results** \\ \hline A distributed routing algorithm & [34] & Divided the surface space into several specific spaces with corresponding logical areas & Avoided dynamics with satellite nodes in the logical region \\ \hline A priority-based adjustable routing, enhanced priority-based adjustable routing & [35] & Balanced load with link utilization and historical data & Reduced redundant traffic data and better utilization of ISLs \\ \hline A virtual node switching algorithm, multi-state virtual network switching algorithm & [36] & Increased number of satellites per orbit to enable soft switching & Reduced data loss and latency \\ \hline A dynamic detection routing scheme & [37] & Lower snapshot latency and fewer path changes & Improved network stability and adaptive capabilities, increased routing computation overhead for the network \\ \hline A load-balancing routing algorithm & [38] & Applied distributed computing to optimize paths & Saved large amounts of on-star computing resources, reduced average queue latency and packet loss \\ \hline A probabilistic routing scheme & [39] & Based on the latitude and longitude of the satellite nodes to select the routing path & Reduced time complexity, achieved load balancing of services \\ \hline A local repair-based, disruption-resistant, on-demand routing scheme & [40] & Applied local repair strategy & Enhanced the real-time performance of the network \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The summary of the works related to the virtual nodes routing algorithms. can be categorized into two phases, the discrete-time virtual topology setup (DTVTS) and the discrete-time path sequence selection (DTPSS). In the DT-VTS, the DT-DVTR algorithm dissociates the LEO satellite network topology into a set of static topologies. In the DTPSS, the classical Dijkstra method is adopted to perform the routing operations. The optimized route is uploaded to each satellite and is corrected at each moment. The DV-DVTR discretizes the topology of a satellite network into a series of static topologies for the first time. However, such an algorithm cannot solve the rerouting problem caused by link switching. The Finite State Automata (FSA) routing algorithm was presented in [45]. The algorithm considers the packet link, satellite network topology, and network traffic to maximize the utilization of the ISL. Like the DV-DVTR, the FSA segments the satellite network topology into a finite number of time slices, and the algorithm is a connection-oriented routing algorithm. Nevertheless, the difference is that FSA treats the topology of each moment block as a \"state\" and models it as a finite state model. The scheme utilizes an iterative approach to find the best link and route assignment for each node.