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International Conference on Internet and Distributed Computing Systems

IDCS 2016: Internet and Distributed Computing Systems pp 513–524Cite as

A Sliding Window Method for Online Tracking of Spatiotemporal Event Patterns

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9864))

Abstract

Online Tracking of Spatiotemporal Event Patterns (OTSEP) is important in the fields of smart home and Internet of Things (IoT), but difficult to be resolved due to various noises. On account of the strong learning capability in noisy environments, Learning Automaton (LA) has been adopted in the existing literature to notify users once a pattern disappears, and suppress the notification to avoid the distraction from noise if a pattern exists. However, the LA-based models require continuous and identical responses from the environment to jump to another action, which lowers their learning speed especially when the noise level is high. This paper proposes a sliding window method, with which the learning speed is stable in different environments. Experimental results show that the learning accuracy and speed are greatly improved over the existing methods in dynamic and noisy environments.

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Acknowledgement

This work is supported by China NSF under Grants No. 61572359, 61272271 and 61332008, US NSF under Grant No. CMMI-1162482, and partly supported by the Fundamental Research Funds for the Central Universities of China (No. 0800219332, 1012015115).

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Authors and Affiliations

  1. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Computer Science and Technology, Tongji University, Shanghai, China

    JunQi Zhang, ShanWen Zhu, Di Zang & MengChu Zhou

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, 07102, USA

    MengChu Zhou

Authors
  1. JunQi Zhang
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  2. ShanWen Zhu
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  3. Di Zang
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  4. MengChu Zhou
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Corresponding author

Correspondence to JunQi Zhang .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology , Wuhan, China

    Wenfeng Li

  2. Central Queensland University , North Rockhampton, Queensland, Australia

    Shawkat Ali

  3. Delft University of Technology , Delft, The Netherlands

    Gabriel Lodewijks

  4. University of Calabria , Rende (CS), Italy

    Giancarlo Fortino

  5. University of Reading , Reading, United Kingdom

    Giuseppe Di Fatta

  6. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  7. CSIRO ICT , Acton, Australia

    Mukaddim Pathan

  8. ICAR-CNR, Rende (CS), Italy

    Antonio Guerrieri

  9. Wuhan University of Technology , Wuhan, China

    Qiang Wang

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© 2016 Springer International Publishing AG

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Zhang, J., Zhu, S., Zang, D., Zhou, M. (2016). A Sliding Window Method for Online Tracking of Spatiotemporal Event Patterns. In: Li, W., et al. Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science(), vol 9864. Springer, Cham. https://doi.org/10.1007/978-3-319-45940-0_48

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  • DOI: https://doi.org/10.1007/978-3-319-45940-0_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45939-4

  • Online ISBN: 978-3-319-45940-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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