Runtime Service Composition Modification Supporting Situational Sensor Data Correlation

  • Chen LiuEmail author
  • Zhongmei Zhang
  • Shouli Zhang
  • Yanbo Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)


Although IoT service and service composition provide effective means to develop IoT applications, the dynamic and time-varying correlation among massive sensors rises up new challenges to the traditional model-based approaches, and the extra uncertainty and complexity of service composition become apparent. This paper proposes a data-driven service composition method based on our previous proactive data service model. We utilize real-time correlation analysis of sensor data to refine model-based service composition at runtime. The correlation among sensor data is usually asynchronous. In this paper, we adopt and improve a Dynamic Time Warping (DTW) algorithm to obtain one-way lag-correlation, and realize dynamic sensor data correlation through refining existing service composition. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate the effectiveness of our service composition method.


Service composition IoT applications Data-driven Lag-correlation 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chen Liu
    • 1
    • 2
    Email author
  • Zhongmei Zhang
    • 1
    • 2
  • Shouli Zhang
    • 1
    • 2
  • Yanbo Han
    • 1
    • 2
  1. 1.Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream DataNorth China University of TechnologyBeijingChina
  2. 2.Cloud Computing Research CenterNorth China University of TechnologyBeijingChina

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