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Erschienen in:

2019 | OriginalPaper | Buchkapitel

Runtime Service Composition Modification Supporting Situational Sensor Data Correlation

verfasst von : Chen Liu, Zhongmei Zhang, Shouli Zhang, Yanbo Han

Erschienen in: Service-Oriented Computing – ICSOC 2018 Workshops

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel The Tentative Research of Hydrological IoT Data Processing System Based on Apache Flink

Nächstes Kapitel A Dynamic Service Adaptation Algorithm for Seamless Integration of Cloud Infrastructure and Edge Devices

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Titel: Runtime Service Composition Modification Supporting Situational Sensor Data Correlation
verfasst von: Chen Liu
Zhongmei Zhang
Shouli Zhang
Yanbo Han
Verlag: Springer International Publishing
Buch: Service-Oriented Computing – ICSOC 2018 Workshops
Print ISBN: 978-3-030-17641-9

Electronic ISBN: 978-3-030-17642-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17642-6_15

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