Advertisement

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)

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.

Keywords

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

Notes

Acknowledgements

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.

References

  1. 1.
    Aldossary, S., Allen, W.: Data security, privacy, availability and integrity in cloud computing: Issues and current solutions. Int. J. Adv. Comput. Sci. Appl. 7(4), 485–498 (2016)Google Scholar
  2. 2.
    Dias, G.M., Bellalta, B., Oechsner, S.: A survey about prediction-based data reduction in wireless sensor networks. ACM Comput. Surv. 49(3), 58 (2016)CrossRefGoogle Scholar
  3. 3.
    Gao, Z., Cheng, W., Qiu, X., Meng, L.: A missing sensor data estimation algorithm based on temporal and spatial correlation. Int. J. Distrib. Sens. Netw. 11(10), 435391 (2015)Google Scholar
  4. 4.
    Gupta, C., et al.: ProtoNN: compressed and accurate kNN for resource-scarce devices. In: Proceedings of the 34th International Conference on Machine Learning, pp. 1331–1340. JMLR.org (2017)Google Scholar
  5. 5.
    Harb, H., Makhoul, A., Laiymani, D., Jaber, A.: A distance-based data aggregation technique for periodic sensor networks. ACM Trans. Sens. Netw. 13(4), 32 (2017)CrossRefGoogle Scholar
  6. 6.
    He, J., Wei, J., Chen, K., Tang, Z., Zhou, Y., Zhang, Y.: Multitier fog computing with large-scale IoT data analytics for smart cities. IEEE Internet of Things J. 5(2), 677–686 (2018)CrossRefGoogle Scholar
  7. 7.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  8. 8.
    Jaques, N., Taylor, S., Sano, A., Picard, R.: Multimodal autoencoder: a deep learning approach to filling in missing sensor data and enabling better mood prediction. In: Proceedings of the 7th International Conference on Affective Computing and Intelligent Interaction, pp. 202–208. IEEE (2017)Google Scholar
  9. 9.
    Kumar, A., Goyal, S., Varma, M.: Resource-efficient machine learning in 2 KB RAM for the Internet of Things. In: Proceedings of the 34th International Conference on Machine Learning, pp. 1935–1944. JMLR.org (2017)Google Scholar
  10. 10.
    Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the Internet of Things with edge computing. IEEE Netw. 32(1), 96–101 (2018)CrossRefGoogle Scholar
  11. 11.
    Luo, X., Zhang, D., Yang, L.T., Liu, J., Chang, X., Ning, H.: A Kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems. Future Gener. Comput. Syst. 61, 85–96 (2016)CrossRefGoogle Scholar
  12. 12.
    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)Google Scholar
  13. 13.
    Raza, U., Camerra, A., Murphy, A.L., Palpanas, T., Picco, G.P.: Practical data prediction for real-world wireless sensor networks. IEEE Trans. Knowl. Data Eng. 27(8), 2231–2244 (2015)CrossRefGoogle Scholar
  14. 14.
    Shen, Z., Zhang, T., Jin, J., Yokota, K., Tagami, A., Higashino, T.: ICCF: an information-centric collaborative fog platform for building energy management systems. IEEE Access 7, 40402–40415 (2019)CrossRefGoogle Scholar
  15. 15.
    Shen, Z., Yokota, K., Tagami, A., Higashino, T.: Development of energy-efficient sensor networks by minimizing sensors numbers with a machine learning model. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 741–746. IEEE (2018)Google Scholar
  16. 16.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  17. 17.
    Tortonesi, M., Govoni, M., Morelli, A., Riberto, G., Stefanelli, C., Suri, N.: Taming the IoT data deluge: an innovative information-centric service model for fog computing applications. Future Gener. Comput. Syst. 93, 888–902 (2019)CrossRefGoogle Scholar
  18. 18.
    Trihinas, D., Pallis, G., Dikaiakos, M.D.: ADMin: adaptive monitoring dissemination for the Internet of Things. In: Proceedings of the IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)Google Scholar
  19. 19.
    Zhang, T., Jin, J., Yang, Y.: RA-FSD: a rate-adaptive fog service delivery platform. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 246–254. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03596-9_16CrossRefGoogle Scholar

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

Personalised recommendations