Criticality-Based Dynamic Topology Optimization for Enhancing Aerial-Marine Swarm Resilience

2025-08-01

AI Summary

Heterogeneous sea-air swarm networks suffer from targeted attacks, frequent communication disruptions, and pronounced structural fragility in adversarial environments. To address these challenges, this paper proposes a two-stage resilience enhancement framework. First, a three-layer dependency network model is constructed, and a novel graph convolutional network (GCN)-based SurBi-Ranking method is designed to jointly rank node and edge criticality dynamically. Second, multi-objective topology reconfiguration optimization is performed using the NSGA-III algorithm. Experimental results demonstrate that, compared with baseline methods such as K-Shell, the proposed approach achieves significantly higher accuracy in identifying critical components. Under attack, network natural connectivity degradation is reduced by approximately 30%, mission success rate improves, and communication reconfiguration cost decreases—thereby effectively ensuring continuity and robustness across multi-phase combat operations.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web search models and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Enhancing resilience in aerial-marine swarm networks
• Dynamic criticality assessment for nodes and edges
• Multi-objective topology optimization for connectivity and efficiency

Innovation

• Three-layer architecture for swarm dependencies representation
• SurBi-Ranking with GCN for dynamic criticality evaluation
• NSGA-III for multi-objective topology optimization