## 1 Introduction
Studies like the aforementioned have involved in-depth research on the characteristics of wireless links, but researchers have not adequately considered the dynamic changes in network topology caused by node mobility, especially in networks with continuous topology changes. Generally, for networks with continuously moving nodes, network topologies of more than two hops are not applicable. This is because longer and more-complex routing paths in such networks make them vulnerable to changes in topology. These changes impair link connectivity and validity, forcing routing protocols to perform route discoveries, updates, and maintenance operations. Consequently, this not only drastically increases routing overhead and communication latency but also significantly degrades overall network performance and efficiency. The authors of [13] propose a link persistence estimation method which can effectively quantify link reliability and provide a basis for path selection. In another study [14], the link quality metric is improved to better reflect the quality of links, and the contribution of the improved metric in enhancing network performance is discussed. The authors of [15] propose the Outlier Bounded Exponential Weighted Moving Average as a metric for reliability-constrained routing. Although the above studies all provide solutions for link state evaluation, the node mobility models they are based on are not accurate enough. The nodes' speed and direction are fixed and unchanging, which limits the applicability of these methods. In the field of routing research, the authors of [16] propose a routing metric based on evolution, generated automatically using genetic programming, which incorporates traffic-related features and mobility for adaptive routing in Mobile Ad Hoc Networks. The authorsof [17] propose a path selection method based on the Hierarchical Manta Ray Foraging Optimization Algorithm, which employs a bio-inspired optimization mechanism to balance the Quality of Service (QoS) metrics and ultimately output the optimal routing path. The authors of [18] propose a hybrid optimization methodology that involves construction of a dual-constraint dynamic clustering model based on mobility metrics and hop count, which selects cluster heads by comprehensively considering fuzzy parameters such as node mobility speed and neighbor density, and finally executes routing through a hybrid cellular automaton approach. The authors of [19] propose an enhanced resilient routing protocol based on traditional routing mechanisms, leveraging blockchain to store node QoS information and employing a Deep Neural Network to select optimal routing nodes. However, the abovementioned studies primarily focus on procedural aspects of routing discovery and maintenance, typically prioritizing the shortest path with the fewest hops or links traversed. As yet, there has not been sufficient emphasis on adequately assessing link persistence as a routing metric to accurately reflect routing reliability. Addressing this challenge is crucial for enhancing the robustness and efficiency of routing in MASS communication scenarios. The authors of [20] propose a design for a routing metric suitable for Flying Ad Hoc Networks, which can effectively control overhead, improve network lifetime, and enhance search success rate. The authors of [21] propose a hybrid interpretable model for land traffic congestion estimation and prediction, which can assist in making rerouting decisions. Although the proposals in the abovementioned papers exhibit differences from maritime communication networks with respect to physical layers and link layers, the methodologies adopted can still provide insights for related work. Motivated by the above discussions, in this paper we study link persistence estimation based on channel capacity, and propose routing metrics for MASS communication scenarios. This study focuses on MASS communication scenarios in civilian fields such as maritime search and rescue, emergency communications, and ocean environment monitoring.