Uncrewed aerial vehicles (UAVs) play a pivotal role in ensuring seamless connectivity for Internet of Things (IoT) devices, particularly in scenarios where conventional terrestrial networks are constrained or temporarily unavailable. However, traditional coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications. To address these limitations, this paper proposes a UAV-assisted framework for real-time detection and recovery of coverage holes in IoT networks. In the proposed scheme, a patrol UAV is first dispatched to identify coverage holes in regions where the operational status of terrestrial base stations (BSs) is uncertain. Once a coverage hole is detected, one or more UAVs acting as aerial BSs are deployed by a satellite or nearby operational BSs to restore connectivity. The UAV swarm is organized based on Delaunay triangulation, enabling scalable deployment and tractable analytical characterization using stochastic geometry. Moreover, a collision-avoidance mechanism grounded in multi-agent system theory ensures safe and coordinated motion among multiple UAVs. Simulation results demonstrate that the proposed framework achieves high efficiency in both coverage-hole detection and on-demand connectivity restoration while significantly reducing operational cost and time.

翻译：无人机在确保物联网设备无缝连接方面发挥着关键作用，尤其在传统地面网络受限或暂时不可用的场景下。然而，传统的覆盖空洞检测方法（如最小化路测）成本高昂、耗时且依赖于过时的无线环境数据，使其难以适用于实时应用。为应对这些局限，本文提出一种无人机辅助的框架，用于物联网网络中覆盖空洞的实时检测与修复。在所提方案中，首先派遣巡逻无人机对地面基站运行状态不确定的区域进行覆盖空洞识别。一旦检测到覆盖空洞，便通过卫星或附近正常运行的基站部署一架或多架充当空中基站的无人机以恢复连接。无人机集群基于Delaunay三角剖分进行组织，从而能够实现可扩展的部署并利用随机几何学进行可处理的解析表征。此外，基于多智能体系统理论的避碰机制确保了多无人机间安全协调的运动。仿真结果表明，所提框架在覆盖空洞检测与按需连接恢复两方面均实现了高效性，同时显著降低了运营成本与时间。