The Internet of Things and AI-based optimization within the Industry 4.0 paradigm

• Autorzy: Mikołajewski Dariusz , Czerniak Jacek M. , Piechowiak Maciej , Węgrzyn-Wolska Katarzyna , Kacprzyk Janusz

Identyfikatory

DOI

10.24425/bpasts.2023.147346

• Języki publikacji: EN

Abstrakty

EN

By reviewing the current state of the art, this paper opens a Special Section titled “The Internet of Things and AI-driven optimization in the Industry 4.0 paradigm”. The topics of this section are part of the broader issues of integration of IoT devices, cloud computing, big data analytics, and artificial intelligence to optimize industrial processes and increase efficiency. It also focuses on how to use modern methods (i.e. computerization, robotization, automation, machine learning, new business models, etc.) to integrate the entire manufacturing industry around current and future economic and social goals. The article presents the state of knowledge on the use of the Internet of Things and optimization based on artificial intelligence within the Industry 4.0 paradigm. The authors review the previous and current state of knowledge in this field and describe known opportunities, limitations, directions for further research, and industrial applications of the most promising ideas and technologies, considering technological, economic, and social opportunities.

Słowa kluczowe

EN

Internet of Things; machine learning; cyber-physical system; industrial Internet of Things; Industry 4.0

PL

Internet rzeczy; nauczanie maszynowe; system cyberfizyczny; przemysłowy Internet rzeczy; Przemysł 4.0

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 1
• Strony: art. no. e147346
• Opis fizyczny: Bibliogr. 47 poz., rys.

Twórcy

autor

Mikołajewski Dariusz

• Faculty of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland

• https://orcid.org/0000-0003-4157-2796

autor

Czerniak Jacek M.

• Faculty of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland

• https://orcid.org/0000-0001-9848-7192

autor

Piechowiak Maciej

• Faculty of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland

• https://orcid.org/0000-0001-8346-5812

autor

Węgrzyn-Wolska Katarzyna

• EFREI Paris Pantheon Assas University, Paris, France

• https://orcid.org/0000-0002-9776-3842

autor

Kacprzyk Janusz

• Systems Research Institute, Polish Academy of Science, Warsaw, Poland

• https://orcid.org/0000-0003-4187-5877

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).