Wireless sensor networks (WSNs) have been rapidly integrated into Internet of Things (IoT) systems, empowering rich and diverse applications such as large-scale environment monitoring. However, due to the random deployment of sensor nodes (SNs), physical topology of the WSNs cannot be controlled and typically remains unknown to the IoT cloud server. Therefore, in order to derive the physical topology at the cloud for effective real-time event detection, a cloud-orchestrated physical topology discovery scheme for large-scale IoT systems using unmanned aerial vehicles (UAVs) is proposed in this paper. More specifically, the large-scale monitoring area is first split into a number of subregions for UAV-enabled data collection. Within the subregions, parallel Metropolis-Hastings random walk (MHRW) is developed to gather the information of WSN nodes, including their IDs and neighbor tables. The collected information is then forwarded to the cloud through UAVs for the initial generation of logical topology. Thereafter, a network-wide 3-D localization algorithm is further developed based on the discovered logical topology and multidimensional scaling method (Topo-MDS), where the UAVs equipped with global positioning system are served as mobile anchors to locate the SNs. Simulation results indicate that the parallel MHRW improves both the efficiency and accuracy of logical topology discovery. In addition, the Topo-MDS algorithm dramatically improves the 3-D location accuracy, as compared to the existing algorithms in the literature.

中文翻译：

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使用无人机进行大规模物联网系统的云编排物理拓扑发现


无线传感器网络（WSN）已快速集成到物联网（IoT）系统中，赋能大规模环境监测等丰富多样的应用。然而，由于传感器节点 (SN) 的随机部署，WSN 的物理拓扑无法控制，并且通常对于物联网云服务器来说仍然未知。因此，为了在云端导出物理拓扑以进行有效的实时事件检测，本文提出了一种针对使用无人机（UAV）的大规模物联网系统的云协调物理拓扑发现方案。更具体地说，首先将大范围的监测区域划分为多个子区域，以便无人机进行数据采集。在子区域内，开发了并行 Metropolis-Hastings 随机游走（MHRW）来收集 WSN 节点的信息，包括它们的 ID 和邻居表。然后通过无人机将收集到的信息转发到云端，以初步生成逻辑拓扑。此后，基于发现的逻辑拓扑和多维缩放方法（Topo-MDS）进一步开发了全网3D定位算法，其中配备全球定位系统的无人机作为移动锚来定位SN。仿真结果表明，并行MHRW提高了逻辑拓扑发现的效率和准确性。此外，与文献中现有的算法相比，Topo-MDS 算法极大地提高了 3D 定位精度。