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    Tytuł:
    Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things
    Autorzy:
    Kliestik, Tomas
    Nica, Elvira
    Durana, Pavol
    Popescu, Gheorghe H.
    Powiązania:
    https://bibliotekanauki.pl/articles/39987975.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    artificial intelligence (AI)
    predictive maintenance (PM)
    Industrial Internet of Things (IIoT)
    time-sensitive networking (TSN)
    big data
    algorithmic decision-making
    Opis:
    Research background: The article explores the integration of Artificial Intelligence (AI) in predictive maintenance (PM) within Industrial Internet of Things (IIoT) context. It addresses the increasing importance of leveraging advanced technologies to enhance maintenance practices in industrial settings. Purpose of the article: The primary objective of the article is to investigate and demonstrate the application of AI-driven PM in the IIoT. The authors aim to shed light on the potential benefits and implications of incorporating AI into maintenance strategies within industrial environments. Methods: The article employs a research methodology focused on the practical implementation of AI algorithms for PM. It involves the analysis of data from sensors and other sources within the IIoT ecosystem to present predictive models. The methods used in the study contribute to understanding the feasibility and effectiveness of AI-driven PM solutions. Findings & value added: The article presents significant findings regarding the impact of AI-driven PM on industrial operations. It discusses how the implementation of AI technologies contributes to increased efficiency. The added value of the research lies in providing insights into the transformative potential of AI within the IIoT for optimizing maintenance practices and improving overall industrial performance.
    Źródło:
    Oeconomia Copernicana; 2023, 14, 4; 1097-1138
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing
    Autorzy:
    Sezgin, Anıl
    Boyacı, Aytuğ
    Powiązania:
    https://bibliotekanauki.pl/articles/2201911.pdf
    Data publikacji:
    2023
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    feature selection
    intrusion detection
    machine learning
    industrial internet of things
    Internet of things
    IIoT
    Opis:
    Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion detection systems (IDS) are used to monitor IIoT networks and identify potential security breaches. Feature selection is an essential step in the IDS process, as it can reduce computational complexity and improve the accuracy of the system. In this research paper, we propose a hybrid feature selection approach for intrusion detection in the IIoT environment using Shapley values and a genetic algorithm-based automated preprocessing technique which has three automated steps including imputation, scaling and feature selection. Shapley values are used to evaluate the importance of features, while the genetic algorithm-based automated preprocessing technique optimizes feature selection. We evaluate the proposed approach on a publicly available dataset and compare its performance with existing state-of-the-art methods. The experimental results demonstrate that the proposed approach outperforms existing methods, achieving high accuracy, precision, recall, and F1-score. The proposed approach has the potential to enhance the performance of IDS in the IIoT environment and improve the overall security of critical industrial systems.
    Źródło:
    Advances in Science and Technology. Research Journal; 2023, 17, 2; 120--135
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forth Industrial Revolution (4 IR): Digital Disruption of Cyber – Physical Systems
    Autorzy:
    Kasza, Joanna
    Powiązania:
    https://bibliotekanauki.pl/articles/1058063.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Forth Industrial Revolution (4IR)
    H2M (human to machine) communication
    Industrial Internet of Things (IIOT)
    Industry 4.0
    Internet of Things (IOT)
    M2H (machine to human)
    M2M (machine to machine)
    SMAC (social
    Smart Manufacturing
    analytics and cloud)
    artificial intelligence (AI)
    cyber-physical systems
    digital disruption (disruptive innovations)
    infosphere
    mobile
    pervasive computing
    philosophy of information
    semantic web
    symbiotic and ubiquitous web
    Opis:
    Article focus of the disruptive character of technological innovations brought by Fourth Industrial Revolution (4IR), with its unprecedented scale and scope, and exponential speed of incoming innovations, described from the point view of ‘unintended consequences’ (cross cutting impact of disruptive technologies across many sectors and aspects of human life). With integration of technology innovations emerging in number of fields including advanced robotics, pervasive computing, artificial intelligence, nano- and bio- technologies, additive and smart manufacturing, Forth Industrial Revolution introduce new ways in which technology becomes embedded not only within the society, economy and culture, but also within human body and mind (described by integration of technologies, collectively referred to as cyber-physical systems). At the forefront of digital transformation, based on cyber physical systems, stands Industry 4.0, referring to recent technological advances, where internet and supporting technologies (embedded systems) are serving as framework to integrate physical objects, human actors, intelligent machines, production lines and processes across organizational boundaries to form new kind of intelligent, networked value chain, called smart factory. Article presents broader context of ‘disruptive changes (innovations)’ accompanying 4IR, that embrace both economical perspective of ‘broader restructuring’ of modern economy and society (described in second part of the article as transition from second to third and forth industrial revolution), and technological perspective of computer and informational science with advances in pervasive computing, algorithms and artificial intelligence (described in third part of article with different stages of web development : web 1.0, web 2.0, web 3.0, web 4.0). What’s more important, article presents hardly ever described in literature, psychological and philosophical perspective, more or less subtle reconfiguration made under the influence of these technologies, determining physical (body), psychological (mind) and philosophical aspect of human existence (the very idea of what it means to be the human), fully depicted in the conclusion of the article. The core element (novelty) is the attempt to bring full understanding and acknowledgment of disruptive innovations’, that “change not only of the what and the how things are done, but also the who we are”, moving beyond economical or technological perspective, to embrace also psychological and philosophical one.
    Źródło:
    World Scientific News; 2019, 134, 2; 118-147
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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