Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 4; pp. 2205 - 2218
• Main Authors: Xiang, Chaocan, Zhang, Zhao, Qu, Yuben, Lu, Dongyu, Fan, Xiaochen, Yang, Panlong, Wu, Fan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Correlation; Data recovery; Edge computing; edge node deployment; intelligent transportation system; Intelligent transportation systems; Iterative methods; low-rank theory; Minimization; Missing data; Optimization; Real time; Real-time systems; Spatiotemporal phenomena; Telecommunication traffic; traffic data recovery; Traffic information; Transforms; Transportation; Transportation networks
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2020.2984658

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.