GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

doi:10.1109/JIOT.2020.2989398

	GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries
	Yu Huang1,2,3 ; Yan Li 4; Zhaofeng Zhang1,2,3 ; Ryan Wen Liu 4
	2020-11-01
发表期刊	IEEE INTERNET OF THINGS JOURNAL (IF:8.2[JCR-2023],9.0[5-Year])
ISSN	2372-2541
卷号	7 期号:11
发表状态	已发表
DOI	10.1109/JIOT.2020.2989398
摘要	The automatic identification system (AIS), an automatic vessel-tracking system, has been widely adopted to perform intelligent traffic management and collision avoidance services in maritime Internet-of-Things (IoT) industries. With the rapid development of maritime transportation, tremendous numbers of AIS-based vessel trajectory data have been collected, which make trajectory data compression imperative and challenging. This article mainly focuses on the compression and visualization of large-scale vessel trajectories and their graphics processing unit (GPU)-accelerated implementations. The visualization was implemented to investigate the influence of compression on vessel trajectory data quality. In particular, the Douglas-Peucker (DP) and kernel density estimation (KDE) algorithms, respectively, utilized for trajectory compression and visualization, were significantly accelerated through the massively parallel computation capabilities of the GPU architecture. Comprehensive experiments on trajectory compression and visualization have been conducted on large-scale AIS data of recording ship movements collected from three different water areas, i.e., the South Channel of Yangtze River Estuary, the Chengshan Jiao Promontory, and the Zhoushan Islands. Experimental results illustrated that: 1) the proposed GPU-based parallel implementation frameworks could significantly reduce the computational time for both trajectory compression and visualization; 2) the influence of compressed vessel trajectories on trajectory visualization could be negligible if the compression threshold was selected suitably; and 3) the Gaussian kernel was capable of generating more appropriate KDE-based visualization performance by comparing with other seven kernel functions.
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收录类别	SCI ; SCIE ; SSCI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/122202
专题	信息科学与技术学院_硕士生信息科学与技术学院_特聘教授组_张钊锋组
作者单位	1.Chinese Academy of Sciences, Shanghai Advanced Research Institute, Shanghai, China 2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China
第一作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Yu Huang,Yan Li,Zhaofeng Zhang,et al. GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries[J]. IEEE INTERNET OF THINGS JOURNAL,2020,7(11).
APA	Yu Huang,Yan Li,Zhaofeng Zhang,&Ryan Wen Liu.(2020).GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries.IEEE INTERNET OF THINGS JOURNAL,7(11).
MLA	Yu Huang,et al."GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries".IEEE INTERNET OF THINGS JOURNAL 7.11(2020).