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Data Gathering with Compressive Sensing for Urban Traffic Sensing in Vehicular Networks

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9426))

Abstract

Vehicular networks have become as an important platform to monitor metropolitan-scale traffic information. However, it is a challenge to deliver and process the huge amount of data from vehicular devices to a data center. By studying a large number of taxi data collected from around 3,000 taxis from Shenzhen city in China, we find that the data readings collected by vehicular devices have a strong spatial correlation. In this paper, we propose a novel scheme based on compressive sensing for traffic monitoring in vehicular networks. In this scheme, we construct a new type of random matrix with only one nonzero element of each row, which can significantly reduce the number of data needed to be transmitted while guaranteeing good reconstruction quality at the data center. Simulation results demonstrate that our scheme can achieve high reconstruction accuracy at a much lower sampling rate.

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Acknowledgements

This work is supported by the Cross-strait joint fund of NSF China (No. U1405251); NSF China (No. 61571129); NSF of Fujian Province (No. 2013J01235, 2015J01250), Foundation of Fujian Educational Committee (No. JA12024), and Research Fund of Fuzhou University (No. 2013-XY-27, 2014-XQ-37, XRC-1460).

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Authors and Affiliations

  1. College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian, China

    Dan Wang, Haifeng Zheng, Xin Chen & Zhonghui Chen

Authors
  1. Dan Wang
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  2. Haifeng Zheng
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  3. Xin Chen
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  4. Zhonghui Chen
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Corresponding author

Correspondence to Haifeng Zheng .

Editor information

Editors and Affiliations

  1. University College of London, London, United Kingdom

    Antonis Bikakis

  2. Fuzhou University, Fuzhou, China

    Xianghan Zheng

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© 2015 Springer International Publishing Switzerland

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Wang, D., Zheng, H., Chen, X., Chen, Z. (2015). Data Gathering with Compressive Sensing for Urban Traffic Sensing in Vehicular Networks. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-26181-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26180-5

  • Online ISBN: 978-3-319-26181-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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