{"ID":2888012,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00688","arxiv_id":"2508.00688","title":"Criticality-Based Dynamic Topology Optimization for Enhancing Aerial-Marine Swarm Resilience","abstract":"Heterogeneous marine-aerial swarm networks encounter substantial difficulties due to targeted communication disruptions and structural weaknesses in adversarial environments. This paper proposes a two-step framework to strengthen the network's resilience. Specifically, our framework combines the node prioritization based on criticality with multi-objective topology optimization. First, we design a three-layer architecture to represent structural, communication, and task dependencies of the swarm networks. Then, we introduce the SurBi-Ranking method, which utilizes graph convolutional networks, to dynamically evaluate and rank the criticality of nodes and edges in real time. Next, we apply the NSGA-III algorithm to optimize the network topology, aiming to balance communication efficiency, global connectivity, and mission success rate. Experiments demonstrate that compared to traditional methods like K-Shell, our SurBi-Ranking method identifies critical nodes and edges with greater accuracy, as deliberate attacks on these components cause more significant connectivity degradation. Furthermore, our optimization approach, when prioritizing SurBi-Ranked critical components under attack, reduces the natural connectivity degradation by around 30%, achieves higher mission success rates, and incurs lower communication reconfiguration costs, ensuring sustained connectivity and mission effectiveness across multi-phase operations.","short_abstract":"Heterogeneous marine-aerial swarm networks encounter substantial difficulties due to targeted communication disruptions and structural weaknesses in adversarial environments. This paper proposes a two-step framework to strengthen the network's resilience. Specifically, our framework combines the node prioritization bas...","url_abs":"https://arxiv.org/abs/2508.00688","url_pdf":"https://arxiv.org/pdf/2508.00688v1","authors":"[\"Ruiyang Huang\",\"Haocheng Wang\",\"Yixuan Shen\",\"Ning Gao\",\"Qiang Ni\",\"Shi Jin\",\"Yifan Wu\"]","published":"2025-08-01T15:03:23Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"eess.SP\"]","methods":"[]","has_code":false}