Industrial Internet of Things (IIoT) and Smart Manufacturing Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (22 April 2022) | Viewed by 10596

Special Issue Editor


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Guest Editor
Lapland University of Applied Sciences, 96300 Rovaniemi, Finland
Interests: industrial internet of things; industrial internet; digital transformation; digital services and systems; measurements and metrics; education

Special Issue Information

Dear Colleagues,

In recent decades, the development of wireless technology driven by digitalisation has dramatically impacted companies’ ways of working and business environment. For example, the Industrial Internet of Things (IIoT) is revolutionizing manufacturing by enabling the acquisition and accessibility of big data at greater speeds than ever before. Digitalisation and effective networking are key factors enabling the manufacturing industry to make a successful shift towards smart manufacturing systems, which will efficiently utilise the potential of new technologies for their business outcomes and value.

In fact, IIoT can greatly improve connectivity, efficiency, scalability, time savings, and cost savings for any kind of industrial organization. New digital technologies such as AI-driven solutions, new means of visualizations (AR, VR), intelligent automation (e.g., advanced robotics), cloud services, new data management and analytics practices (e.g., predictive analytics and AI) etc., offer new possibilities to improve companies’ productivity, quality, flexibility and service, self-optimizing operations and effectiveness. Digitalisation and IIoT have brought new business opportunities and new business models but also changed the roles of operators in a value chain and so removed traditional intermediaries in the supply chain while creating new ones. This is due to, for example, direct access to consumers and the increased use of mobile devices or creation of new kinds of ecosystems. Any advances in manufacturing technology and new business models boosted by digitalisation are continuously providing new opportunities and creating added value for the whole manufacturing value chain. Manufacturing industry is going towards Smart Manufacturing Systems with IIoT.

This Special Issue is devoted to topics related to recent trends and progress made in the field of Industrial Internet of Things and Smart Manufacturing Systems. We would like to gather researchers from different disciplines and methodological backgrounds to discuss new ideas, research questions, recent results, and future challenges in this emerging area of research and public interest.

Dr. Maarit Tihinen
Guest Editor

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Keywords

  • Artificial Intelligence (AI)
  • Augmented Reality (AR)
  • Business ecosystems
  • Business models
  • Data management and analytics
  • Digital transformation
  • Industrial Internet of Things (IIoT)
  • Predictive analytics
  • Robotic Process Automation
  • Robotics
  • Virtual Reality (VR)

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Published Papers (3 papers)

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Research

19 pages, 2009 KiB  
Article
An SDN-Enabled Architecture for IT/OT Converged Networks: A Proposal and Qualitative Analysis under DDoS Attacks
by Luca Foschini, Valentina Mignardi, Rebecca Montanari and Domenico Scotece
Future Internet 2021, 13(10), 258; https://doi.org/10.3390/fi13100258 - 8 Oct 2021
Cited by 9 | Viewed by 2990
Abstract
Real-time business practices require huge amounts of data directly from the production assets. This new thirst for accurate and timely data has forced the convergence of the traditionally business-focused information technology (IT) environment with the production-focused operational technology (OT). Recently, software-defined network (SDN) [...] Read more.
Real-time business practices require huge amounts of data directly from the production assets. This new thirst for accurate and timely data has forced the convergence of the traditionally business-focused information technology (IT) environment with the production-focused operational technology (OT). Recently, software-defined network (SDN) methodologies have benefitted OT networks with enhanced situational awareness, centralized configuration, deny-by-default forwarding rules, and increased performance. What makes SDNs so innovative is the separation between the control plane and the data plane, centralizing the command in the controllers. However, due to their young age, the use of SDNs in the industry context has not yet matured comprehensive SDN-based architectures for IT/OT networks, which are also resistant to security attacks such as denial-of-service ones, which may occur in SDN-based industrial IoT (IIoT) networks. One main motivation is that the lack of comprehensive SDN-based architectures for IT/OT networks making it difficult to effectively simulate, analyze, and identify proper detection and mitigation strategies for DoS attacks in IT/OT networks. No consolidated security solutions are available that provide DoS detection and mitigation strategies in IT/OT networks. Along this direction, this paper’s contributions are twofold. On the one hand, this paper proposes a convergent IT/OT SDN-based architecture applied in a real implementation of an IT/OT support infrastructure called SIRDAM4.0 within the context of the SBDIOI40 project. On the other hand, this paper proposes a qualitative analysis on how this architecture works under DoS attacks, focusing on what the specific problems and vulnerabilities are. In particular, we simulated several distributed denial-of-service (DDoS) attack scenarios within the context of the proposed architecture to show the minimum effort needed by the attacker to hack the network, and our obtained experimental results show how it is possible to compromise the network, thus considerably worsening the performance and, in general, the functioning of the network. Finally, we conclude our analysis with a brief description on the importance of employing machine learning approaches for attack detection and for mitigation techniques. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) and Smart Manufacturing Systems)
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16 pages, 3469 KiB  
Article
Digital Manufacturing Challenges Education—SmartLab Concept as a Concrete Example in Tackling These Challenges
by Maarit Tihinen, Ari Pikkarainen and Jukka Joutsenvaara
Future Internet 2021, 13(8), 192; https://doi.org/10.3390/fi13080192 - 26 Jul 2021
Cited by 6 | Viewed by 3536
Abstract
Digitalization is boosting the manufacturing industry’s shift to smart manufacturing systems, which will efficiently utilize the potential of new technologies for their business outcomes and value. However, the literature shows that manufacturing companies have implemented very little digital technology due to a lack [...] Read more.
Digitalization is boosting the manufacturing industry’s shift to smart manufacturing systems, which will efficiently utilize the potential of new technologies for their business outcomes and value. However, the literature shows that manufacturing companies have implemented very little digital technology due to a lack of the required knowledge and competences. Increasingly, interconnected, digitalized, and complex processes lead to new skill requirements in companies and thereafter also of their workforce’s training needs to respond to the smart manufacturing’s new great expectations. The article provides concrete examples of tackling challenges in education arising from digital manufacturing. The case study introduced in this article concerns the additive manufacturing (AM) method, which is expected to give rise to significant changes in various industrial fields, including digital manufacturing. Advances in digital manufacturing requires skilled professionals who are aware of the possibilities and potential of the latest technology. Education therefore needs to be developed. This article points out that the built learning and development environment, SmartLab, supports multidisciplinary approaches and close collaboration between several stakeholders like companies, engineering education courses, students, and RDI actors. The SmartLab concept is thus also expected to provide a remarkable competitive advantage for business in the region. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) and Smart Manufacturing Systems)
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16 pages, 455 KiB  
Article
AOSR 2.0: A Novel Approach and Thorough Validation of an Agent-Oriented Storage and Retrieval WMS Planner for SMEs, under Industry 4.0
by Fareed Ud Din, David Paul, Joe Ryan, Frans Henskens and Mark Wallis
Future Internet 2021, 13(6), 155; https://doi.org/10.3390/fi13060155 - 15 Jun 2021
Cited by 7 | Viewed by 2986
Abstract
The Fourth Industrial Revolution (Industry 4.0), with the help of cyber-physical systems (CPS), the Internet of Things (IoT), and Artificial Intelligence (AI), is transforming the way industrial setups are designed. Recent literature has provided insight about large firms gaining benefits from Industry 4.0, [...] Read more.
The Fourth Industrial Revolution (Industry 4.0), with the help of cyber-physical systems (CPS), the Internet of Things (IoT), and Artificial Intelligence (AI), is transforming the way industrial setups are designed. Recent literature has provided insight about large firms gaining benefits from Industry 4.0, but many of these benefits do not translate to SMEs. The agent-oriented smart factory (AOSF) framework provides a solution to help bridge the gap between Industry 4.0 frameworks and SME-oriented setups by providing a general and high-level supply chain (SC) framework and an associated agent-oriented storage and retrieval (AOSR)-based warehouse management strategy. This paper presents the extended heuristics of the AOSR algorithm and details how it improves the performance efficiency in an SME-oriented warehouse. A detailed discussion on the thorough validation via scenario-based experimentation and test cases explain how AOSR yielded 60–148% improved performance metrics in certain key areas of a warehouse. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) and Smart Manufacturing Systems)
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