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How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

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  • Virginia Pilloni

    (Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy)

Abstract

We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products’ quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.

Suggested Citation

  • Virginia Pilloni, 2018. "How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0," Future Internet, MDPI, vol. 10(3), pages 1-14, March.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:3:p:24-:d:134086
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    References listed on IDEAS

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    1. Matteo Mallus & Giuseppe Colistra & Luigi Atzori & Maurizio Murroni & Virginia Pilloni, 2017. "Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith," Sustainability, MDPI, vol. 9(2), pages 1-21, February.
    2. Fogliatto, Flavio S. & da Silveira, Giovani J.C. & Borenstein, Denis, 2012. "The mass customization decade: An updated review of the literature," International Journal of Production Economics, Elsevier, vol. 138(1), pages 14-25.
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    Cited by:

    1. Radosław Drozd & Radosław Wolniak, 2021. "Metrisable assessment of the course of stream-systemic processes in vector form in industry 4.0," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2161-2176, December.
    2. Damianos P. Sakas & Nikolaos Th. Giannakopoulos, 2021. "Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    3. Lijun Zhang & Kai Liu & Jian Liu, 2018. "Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes," Future Internet, MDPI, vol. 10(8), pages 1-10, July.
    4. Mihui Kim & Junhyeok Yun, 2020. "Development of User-Participatory Crowdsensing System for Improved Privacy Preservation," Future Internet, MDPI, vol. 12(3), pages 1-19, March.
    5. Anna Kwiotkowska & Magdalena Gębczyńska, 2022. "Job Satisfaction and Work Characteristics Combinations in Industry 4.0 Environment—Insight from the Polish SMEs in the Post–Pandemic Era," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    6. Domaszewicz, Jaroslaw & Parzych, Dariusz, 2022. "Intra-Company Crowdsensing: Datafication with Human-in-the-Loop," MPRA Paper 112608, University Library of Munich, Germany.
    7. Aldona Kluczek & Patrycja Żegleń & Daniela Matušíková, 2021. "The Use of Prospect Theory for Energy Sustainable Industry 4.0," Energies, MDPI, vol. 14(22), pages 1-29, November.
    8. Iñigo Pombo & Leire Godino & Jose Antonio Sánchez & Rafael Lizarralde, 2020. "Expectations and limitations of Cyber-Physical Systems (CPS) for Advanced Manufacturing: A View from the Grinding Industry," Future Internet, MDPI, vol. 12(9), pages 1-15, September.

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