A Dynamic Cascading Failure Model for LEO Satellite Networks

The advancement of space technology has positioned LEO satellite networks as a pivotal solution for global digitalization. However, satellites can fail due to various internal and external threats. Their limited buffer capacity and processing ability can exacerbate failures, posing a risk of cascading failures to the entire network. To delve deeper into the cascading process of LEO satellite networks, we have developed a dynamic cascading failure model. We propose a method to calculate the minimum time required for satellite movement to cover discrete zone units on Earth. By considering the dynamic changes in satellite coverage, we model the probability of user data reaching satellites using a Poisson process. Additionally, we have established models for satellites’ processing ability and buffer capacity to depict a more realistic satellite state transition. Satellites can become congested or even overloaded when they receive an excessive amount of data and require a corrective phase before returning to standard operations. We present a dynamic cascading algorithm, ensuring that the sequence of observations doesn’t impact the final cascading process outcome. Through detailed case studies involving three constellations, we found that cascading failures tend to propagate mainly to satellites within two hops. Smaller constellations are more susceptible to periodic cascading failures, while mega-constellations might experience rapid, severe avalanche effects. These findings offer practical insights for satellite network managers aiming to mitigate cascading risks.