ESDA: An Energy-Saving Data Analytics Fog Service Platform

  • Tiehua ZhangEmail author
  • Zhishu Shen
  • Jiong Jin
  • Atsushi Tagami
  • Xi Zheng
  • Yun Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)


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.


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



This work is partly supported by Australian Government Research Training Program Scholarship, Australian Research Council Discovery Project Grant DP180100212 and NICT (Contract No. 19103), Japan.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tiehua Zhang
    • 1
    Email author
  • Zhishu Shen
    • 2
  • Jiong Jin
    • 1
  • Atsushi Tagami
    • 2
  • Xi Zheng
    • 3
  • Yun Yang
    • 1
  1. 1.School of Software and Electrical EngineeringSwinburne University of TechnologyMelbourneAustralia
  2. 2.KDDI Research, Inc.Fujimino-shiJapan
  3. 3.Department of ComputingMacquarie UniversitySydneyAustralia

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