ESDA

an energy-saving data analytics fog service platform

Tiehua Zhang*, Zhishu Shen, Jiong Jin, Atsushi Tagami, Xi Zheng, Yun Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

The volume of heterogeneous data collected through a variety of sensors is growing exponentially. With the increasing popularity of providing real-time data analytics services at the edge of the network, the process of harvesting and analysing sensor data is thus an inevitable part of enhancing the service experience for users. In this paper, we propose a fog-empowered data analytics service platform to overcome the frequent sensor data loss issue through a novel deep autoencoder model while keeping the minimum energy usage of the managed sensors at the same time. The platform incorporates several algorithms with the purpose of training the individual local fog model, saving the overall energy consumption, as well as operating the service process. Compared with other state-of-the-art techniques for handling missing sensor data, our platform specialises in finding the underlying relationship among temporal sensor data series and hence provides more accurate results on heterogeneous data types. Owing to the superior inference capability, the platform enables the fog nodes to perform real-time data analytics service and respond to such service request promptly. Furthermore, the effectiveness of the proposed platform is verified through the real-world indoor deployment along with extensive experiments.

Original languageEnglish
Title of host publicationService-Oriented Computing
Subtitle of host publication17th International Conference, ICSOC 2019, Proceedings
EditorsSami Yangui, Ismael Bouassida Rodriguez, Khalil Drira, Zahir Tari
Place of PublicationSwitzerland
PublisherSpringer
Pages171-185
Number of pages15
ISBN (Electronic)9783030337025
ISBN (Print)9783030337018
DOIs
Publication statusPublished - Oct 2019
Event17th International Conference on Service-Oriented Computing, ICSOC 2019 - Toulouse, France
Duration: 28 Oct 201931 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11895 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Service-Oriented Computing, ICSOC 2019
CountryFrance
CityToulouse
Period28/10/1931/10/19

Keywords

  • Deep autoencoder
  • Energy-saving algorithm
  • Fog computing
  • Service-oriented networking

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  • Cite this

    Zhang, T., Shen, Z., Jin, J., Tagami, A., Zheng, X., & Yang, Y. (2019). ESDA: an energy-saving data analytics fog service platform. In S. Yangui, I. Bouassida Rodriguez, K. Drira, & Z. Tari (Eds.), Service-Oriented Computing: 17th International Conference, ICSOC 2019, Proceedings (pp. 171-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11895 LNCS). Switzerland: Springer. https://doi.org/10.1007/978-3-030-33702-5_13