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A Survey of Internet of Things and Big Data integrated Solutions for Industrie 4.0

: Al-Gumaei, Khaled; Schuba, Kornelia; Friesen, Andrej; Heymann, Sascha; Pieper, Carsten; Pethig, Florian; Schriegel, Sebastian


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018. Proceedings : Politecnico di Torino, Torino, Italy, 04-07 September 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7108-5
ISBN: 978-1-5386-7107-8
ISBN: 978-1-5386-7109-2
International Conference on Emerging Technologies and Factory Automation (ETFA) <23, 2018, Torino>
Fraunhofer IOSB ()
Big Data; Cloud Computing; Internet of Things; Industrial IoT; Industrie 4.0; Industrial Automation

The industrial internet of things (IIoT) is growing at an exponential rate generating massive amounts of industrial data. This data must be leveraged to support business and operational goals. As a result, there is an urgent need for adopting big data technologies to enable data analytics in industrial automation. This paper explores interrelations between IIoT and big data technologies and how they work together to generate business insights from industrial data. Additionally, requirements for cloud-based solutions are derived from the Industrie 4.0 use case scenario value-based-services, focusing on condition monitoring and predictive maintenance services. A survey of selected cloud-based platforms is conducted to examine how these platforms meet the requirements derived from the use case. Results show that existing general cloud platforms should adopt more IIoT applications and platforms, while existing industrial cloud platforms should add big data frameworks to their portfolio. Finally, an architecture for integrating cloud-based IIoT and big data solutions is introduced and issues regarding the use of public cloud for IIoT applications are discussed.