Abstract
Billions of sensing devices have been connected to the Internet of Things (IoT), generating a large volume of data that can be turned into valuable insights for many applications. Location information is critical for many IoT applications. However, most sensor devices are randomly deployed and locations are unknown. Thus, it is a challenging issue to discover the physical topology of the IoT system consisted of thousands of low-cost sensor devices. In this paper, a Vehicles joint UAVs Topology Discovery (VUTD) scheme is proposed that can discover the physical topology with low-cost and accuracy. There are two main steps in VUTD scheme: (1) Vehicles are used as mobile anchors to assist adjacent sensor devices in positioning. They are also used to collect logical topology information of the IoT system. The collected logical topology information and location information can be combined into physical topology information that will be sent to the cloud platform through vehicles. (2) The cloud platform analyzes the received information to determine the area where the physical topology discovery is not completed. Then, the cloud platform dispatches the UAV as a flight anchor to locate these points. Experiments based on realworld taxi trajectory are conducted to verify the effectiveness of VUTD scheme. The experimental results show that the VUTD scheme has better performance. Compared with the VTD scheme, the localization ratio is increased by up to 13.6%, and the mean localization error is reduced by up to 90.78%. Compared with UTD, the cost of location discovery is reduced by up to 77.7%.
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This work was supported in part by the National Natural Science Foundation of China (61772554).
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This article is part of the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge
Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong
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Teng, H., Ota, K., Liu, A. et al. Vehicles joint UAVs to acquire and analyze data for topology discovery in large-scale IoT systems. Peer-to-Peer Netw. Appl. 13, 1720–1743 (2020). https://doi.org/10.1007/s12083-020-00879-5
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DOI: https://doi.org/10.1007/s12083-020-00879-5