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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.00688 |
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| _version_ | 1866912515174170624 |
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| author | Huang, Ruiyang Wang, Haocheng Shen, Yixuan Gao, Ning Ni, Qiang Jin, Shi Wu, Yifan |
| author_facet | Huang, Ruiyang Wang, Haocheng Shen, Yixuan Gao, Ning Ni, Qiang Jin, Shi Wu, Yifan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Criticality-Based Dynamic Topology Optimization for Enhancing Aerial-Marine Swarm Resilience Huang, Ruiyang Wang, Haocheng Shen, Yixuan Gao, Ning Ni, Qiang Jin, Shi Wu, Yifan Networking and Internet Architecture Signal Processing 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. |
| title | Criticality-Based Dynamic Topology Optimization for Enhancing Aerial-Marine Swarm Resilience |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2508.00688 |