To address partial node failures in unmanned aerial vehicle swarms, self-healing communication techniques are commonly employed to restore backbone connectivity while preserving area coverage. However, existing heuristic methods struggle to scale under large-scale failures and dynamic conditions, while learning-based approaches often suffer from spatial collapse, resulting in significant coverage loss. To overcome these limitations, we propose a resilient self-healing framework that enables rapid connectivity recovery and wide-area coverage through a divide-and-conquer strategy. First, we introduce a buffered dynamic virtual force expansion mechanism that categorizes pairwise distances into repulsive, neutral, and attractive zones, allowing nodes to disperse appropriately while preserving communication links and maintaining safety buffers. Subsequently, we design a multipartite graph convolution module to reason over subnetwork-level interactions and facilitate cross-subnetwork reconnection with global structural awareness. Finally, we develop an adaptive fusion strategy that combines both outputs with time-aware weighting to generate the final motion decisions. Experimental results in both random and uniform deployment scenarios demonstrate that our approach outperforms many state-of-the-art methods in terms of connectivity restoration speed and communication coverage.

The robustness of Internet of Things (IoT) communication topologies against internal failures and external perturbations is a fundamental prerequisite for maintaining system stability. This paper studies IoT topology robustness from two perspectives: robustness metric and optimization. Existing robustness metrics, primarily based on the maximum connected subgraph, neglect contributions from other connected subgraphs, thereby inadequately capturing dynamic topological changes. To address this issue, we propose a robustness metric based on topology data reachability, which sensitively reflects the data transmission capability of an IoT topology under arbitrary perturbations. Regarding robustness optimization, most existing methods adopt centralized strategies that rely on global information, resulting in inefficiencies and limited adaptability in decentralized IoT environments. We propose DecTRO, a Decentralized Topology Robustness Optimization method for IoT via multi-agent graph reinforcement learning. To mitigate partial observability, DecTRO employs a scalable graph attention network enhanced with multi-modal sampling, which aggregates cross-agent information and captures spatiotemporal correlations. Furthermore, a topology robustness-oriented node sampling method is introduced to reduce action-space complexity and accelerate convergence, while a decentralized heuristic reward function enables efficient online decentralized learning. Experimental results show that DecTRO achieves up to two orders of magnitude (5–125×) greater improvement in robustness per unit time compared with state-of-the-art baselines, striking a favorable balance between robustness enhancement and computational efficiency.

Network failures, whether due to random disruptions or malicious attacks, pose significant challenges for uncrewed aerial vehicle (UAV) swarm networks. One critical concern is determining which failed UAVs to recover or replace under limited resource conditions to enhance the robustness of their communication networks. Current research primarily considers static structural characteristics of the network and struggles to uncover deep features that influence network robustness, and the efficiency cannot meet the real-time needs in UAV swarm scenarios. To address these issues, we introduce a Prioritized Recovery strategy for failed nodes based on graph reinforcement learning (PRGRL). This approach integrates a random SAmpling neighbor method with a multihead attention mechanism to create a novel graph convolutional kernel (SAGCK). This kernel is designed to extract global structural information and relative positional information of nodes within the graph. Additionally, we develop a deep policy network (DPN) that explores the intricate relationships between graph-level and node embedding features, enabling the assessment of nodes’ impact on overall robustness. PRGRL’s network parameters are automatically updated and optimized using scalable deep reinforcement learning. Importantly, PRGRL prioritizes the recovery of boundary nodes within connected components to enhance network robustness further. Our experiments, conducted on both simulated and real-world networks, demonstrate that PRGRL outperforms existing methods of robustness enhancement across various recovery ratios, attack strategies, and network sizes while delivering superior real-time performance.

The Internet of Things (IoT) devices have limited resources and are vulnerable to attacks, so optimizing their network topology to resist random failures and malicious attacks has become a key issue. The scale-free network model has strong resistance to random attacks, but it is very vulnerable to malicious attacks. The existing studies mostly adopt heuristic algorithms to optimize the ability of scale-free networks to resist malicious attacks, but their high computational cost cannot meet the timeliness requirements of the real IoT. Therefore, this paper proposes an intelligent topology robustness optimization model based on a graph convolutional network (ROGCN). The model extracts the onion-like structural features of the highly robust network topology from the data set through supervised learning, and on this basis, different search strategies are designed to meet the needs of different IoT scenarios. The extensive experimental results demonstrate that ROGCN can more effectively improve the robustness of scale-free IoT networks against malicious attacks compared to two existing heuristic algorithms, with a lower computational cost.