Abstract
Industrial IoT data analysis is an essential means of obtaining important information in efficient smart factory operation. IoT devices connected to Smart Factory produce large amount of data from variety of mechanical facilities that actually operate. Because the collected data can be analyzed in real time, optimizing plant operations can be optimized, predicting maintenance schedules for mechanical facilities or quickly replacing equipment with faulty ones. In addition, various information needed for efficient operation can be obtained, such as improving the quality of the products produced. Using 5G wireless network and fog node and cloud computing, this paper introduces a new platform that supports efficient analysis of big data collected in smart factories. Leveraging various resources of 5G wireless network, it provides an optimized environment for collecting IoT data such as size, speed, delay and variety. It also provides big data analytic services through fog nodes and cloud computing and addresses various requirements for data collection, processing, analysis and management. This paper describes the requirements and design components of the proposed platform. Introduce case studies using data sets obtained from smart factories to validate the platform and provide meaningful results. The experimental results clearly show the benefits and practicalities of the platform.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Palattella, M.R., et al.: IoT in the 5G era: enablers, architecture, and business models. IEEE J. Sel. Areas Commun. 34(3), 510–527 (2016)
Agiwal, M., et al.: Next generation 5G wireless networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 18(3), 1617–1655 (2016)
Cai, H., et al.: IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet Things J. 4(1), 75–87 (2017)
Kang, B., et al.: Internet of everything: a large-scale autonomic IoT gateway. IEEE Trans. Multi-Scale Comput. Syst. 3(3), 206–214 (2017)
Lohokare, J., et al.: An IoT ecosystem for the implementation of scalable wireless home automation systems at smart city level. In: TENCON 2017–2017 IEEE Region 10 Conference, pp. 1503–1508 (2017)
Mehdipour, F., et al.: FOG-Engine: towards big data analytics in the fog. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, pp. 640–646 (2016)
Li, J., et al.: Latency estimation for fog-based internet of things. In: 2017 27th International Telecommunication Networks and Applications Conference, pp. 1–6 (2017)
Hou, L., et al.: Internet of things cloud: architecture and implementation. IEEE Commun. Mag. 54(15), 32–39 (2016)
Hong, H.J., et al.: Supporting IoT analytics in a fog computing platform. In: International Conference on Cloud Computing Technology and Science, pp. 138–145 (2017)
Patel, P., et al.: On using the intelligent edge for IoT analytics. IEEE Intell. Syst. 32(5), 64–69 (2017)
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2017R1A6A3A11035613).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, Y., Park, B., Jeong, J. (2019). Fog Based IIoT Architecture Based on Big Data Analytics for 5G-networked Smart Factory. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11620. Springer, Cham. https://doi.org/10.1007/978-3-030-24296-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-24296-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24295-4
Online ISBN: 978-3-030-24296-1
eBook Packages: Computer ScienceComputer Science (R0)