期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 7, 期 4, 页码 2205-2218出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2020.2984658
关键词
Correlation; Spatiotemporal phenomena; Transportation; Real-time systems; Edge computing; Transforms; Minimization; Intelligent transportation systems; Telecommunication traffic; Edge computing; intelligent transportation system; edge node deployment; traffic data recovery; low-rank theory
资金
- NSF of China [61872447, 61632010, 61772546, 61702525, 61672038]
- Natural Science Foundation of Chongqing [CSTC2018JCYJA1879]
- National Postdoctoral Program for Innovative Talents of China [BX20190202]
Intelligent Transportation Systems (ITSs) have been widely deployed to provide traffic sensing data for a variety of smart traffic applications. However, the inevitable and ubiquitous missing data potentially compromises the performance of ITSs and even undermines the traffic applications. Therefore, accurate and real-time traffic data recovery is crucial to ITSs and its related services, especially for large-scale traffic networks. To leverage the characteristics in transportation networks for data recovery, we first conduct experimental explorations on a large-scale traffic dataset of an ITS and further quantify the spatiotemporal correlations of traffic data. Inspired by the observation results, we propose GTR, an edGe computing-empowered system for large-scale Traffic data recovery with low-Rank theory. GTR leverages the decentralized computing power of edge nodes to process massive traffic data from hundreds of traffic stations for accurate and real-time recovery. Specifically, we first propose a suboptimal edge node deployment algorithm with a theoretical performance guarantee, by exploiting the supermodularity in the NP-hard joint-optimization problem. Furthermore, to leverage the low-rank nature of traffic data, we transform the data recovery problem into a low-rank minimization problem, then utilize the fixed-point continuation iterative scheme to capture spatiotemporal correlations for accurate traffic recovery. Finally, the extensive trace-driven evaluations show that GTR only needs at most 5.7% extra total cost compared to the optimal deployment, while outperforming four baseline methods by 63.8% improvement in terms of traffic data recovery accuracy.
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