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.