Edge computing-empowered large-scale traffic data recovery leveraging low-rank theory

Publisher:
Institute of Electrical and Electronics Engineers
Publication Type:
Journal Article
Citation:
IEEE Transactions on Network Science and Engineering, 2020, 7, (4), pp. 2205-2218
Issue Date:
2020-10-01
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MainFile(TNSE).pdfAccepted manuscript5.32 MB
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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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