uncrewed aerial vehicle (UAV)–enabled Internet of Things UAV-enabled Internet of Things (UIoT) is composed of multiple UAV swarms, which are vulnerable to external shocks in variable environments. Currently, the majority of research on UIoT resilience primarily emphasizes internal resource reconfiguration to maintain mission execution, while overlooking the importance of performance recovery with logistics support. This article proposes a novel UIoT resilience evaluation framework that comprehensively analyzes the performance and resilience by considering various shocks and external resource support strategies. First, a UIoT modeling based on a multimodular community structure is established, and the evolutionary behaviors of UIoT are investigated by considering their cooperative adaptation mechanisms under dynamically changing operational conditions. Second, with internal allocation and external logistics support, a multistate resilience mechanism is developed, overcoming the limitations of prioritizing internal reconfiguration strategies. Third, dynamic node connection mechanisms and resource allocation strategies are proposed to maximize resilience recovery efficiency. Finally, a collaborative detection case involving 20-UAVs-IoT is illustrated to verify the effective resilience evaluation and resource allocation. The proposed method improves the resilience and performance recovery of UIoT networks, providing support for resilience evaluation and resource allocation in variable environments.

Maritime cross-domain unmanned system-of-systems (MCUSoS), featuring multi-domain collaboration, wide-area coverage, and flexible deployment, plays a vital role in missions such as maritime search and rescue, marine environmental monitoring, and terrain reconnaissance. MCUSoS enables collaborative detection by coordinating heterogeneous unmanned clusters across the aerial, surface, and underwater domains. However, this capability is vulnerable to degradation under cross-domain heterogeneity, communication constraints, and external disturbances such as node failures, link disruptions and malicious interference. To address these challenges, this paper proposes an integrated framework for collaborative detection capability evaluation and resilience enhancement of MCUSoS in multi-disturbance environments. Firstly, a system-of-systems architecture is established by incorporating formation detection modes and multi-level collaborative relationships to characterize its collaborative detection capabilities. Second, a capability evaluation model is developed from the capabilities of collaboration and detection. Based on this, a multi-stage resilience evaluation mechanism is proposed to quantify MCUSoS resilience under three disturbance modes. Additionally, a resilience enhancement strategy combining internal reconfiguration with the external deployment of supplementary detection nodes is designed to recover MCUSoS performance in multi-disturbance environments. Finally, a case study involving 12 clusters of MCUSoS is conducted to validate the effectiveness of the proposed methods. The results demonstrate that the proposed resilience enhancement strategy achieves a recovery rate of up to 74% in the disintegration circle attack scenario and consistently improves the resilience of the MCUSoS under targeted attacks, with the resilience value under low-frequency attacks being 148% higher than that under high-frequency attacks. These findings provide a quantitative basis for resilience evaluation and enhancement in dynamic scenarios.