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.
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