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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使用无人机的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）已被快速集成到物联网（IoT）系统中，从而为诸如大型环境监视之类的丰富应用提供了支持。但是，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IoT云服务器仍然未知。因此，为了在云上导出物理拓扑以进行有效的实时事件检测，本文提出了一种使用无人飞行器（UAV）的大规模物联网系统的云编排物理拓扑发现方案。更具体地说，首先将大规模监视区域划分为多个子区域，以支持启用了无人机的数据收集。在该次区域内，开发了并行的Metropolis-Hastings随机游走（MHRW）来收集WSN节点的信息，包括其ID和邻居表。收集的信息然后通过UAV转发到云，以初始生成逻辑拓扑。此后，基于发现的逻辑拓扑和多维缩放方法（Topo-MDS），进一步开发了全网络3-D定位算法，其中，配备有全球定位系统的UAV用作移动锚来定位SN。仿真结果表明，并行MHRW可以提高逻辑拓扑发现的效率和准确性。此外，与文献中的现有算法相比，Topo-MDS算法大大提高了3-D定位精度。此后，基于发现的逻辑拓扑和多维缩放方法（Topo-MDS），进一步开发了全网络3-D定位算法，其中，配备有全球定位系统的UAV用作移动锚来定位SN。仿真结果表明，并行MHRW可以提高逻辑拓扑发现的效率和准确性。此外，与文献中的现有算法相比，Topo-MDS算法大大提高了3-D定位精度。此后，基于发现的逻辑拓扑和多维缩放方法（Topo-MDS），进一步开发了全网络3-D定位算法，其中，配备有全球定位系统的UAV用作移动锚来定位SN。仿真结果表明，并行MHRW可以提高逻辑拓扑发现的效率和准确性。此外，与文献中的现有算法相比，Topo-MDS算法大大提高了3D定位精度。仿真结果表明，并行MHRW可以提高逻辑拓扑发现的效率和准确性。此外，与文献中的现有算法相比，Topo-MDS算法大大提高了3-D定位精度。仿真结果表明，并行MHRW可以提高逻辑拓扑发现的效率和准确性。此外，与文献中的现有算法相比，Topo-MDS算法大大提高了3-D定位精度。