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Review

Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review

by
Cristian Rocha-Jácome
,
Ramón González Carvajal
*,
Fernando Muñoz Chavero
,
Esteban Guevara-Cabezas
and
Eduardo Hidalgo Fort
Department of Electronics Engineering, University of Seville, 41092 Seville, Spain
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(1), 66; https://doi.org/10.3390/s22010066
Submission received: 3 November 2021 / Revised: 18 December 2021 / Accepted: 21 December 2021 / Published: 23 December 2021
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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Versions Notes

Abstract

:
Currently, the concept of Industry 4.0 is well known; however, it is extremely complex, as it is constantly evolving and innovating. It includes the participation of many disciplines and areas of knowledge as well as the integration of many technologies, both mature and emerging, but working in collaboration and relying on their study and implementation under the novel criteria of Cyber–Physical Systems. This study starts with an exhaustive search for updated scientific information of which a bibliometric analysis is carried out with results presented in different tables and graphs. Subsequently, based on the qualitative analysis of the references, we present two proposals for the schematic analysis of Industry 4.0 that will help academia and companies to support digital transformation studies. The results will allow us to perform a simple alternative analysis of Industry 4.0 to understand the functions and scope of the integrating technologies to achieve a better collaboration of each area of knowledge and each professional, considering the potential and limitations of each one, supporting the planning of an appropriate strategy, especially in the management of human resources, for the successful execution of the digital transformation of the industry.

1. Introduction

In a globalized and highly competitive world, an industry that is not willing to innovate, to constantly improve its processes or to use emerging technologies may be condemning itself to disappear. That is why the concept of Industry 4.0 has become a fundamental pillar, both in current research and in its application in the industrial sector. There is a classification made by The IMD World Digital Competitiveness Ranking 2021, which can be a benchmark of the technological reality of industrialized countries. This ranking annually conducts studies that measure the capacity and readiness of 63 economies to adopt and explore digital technologies for economic and social transformation, considering factors, such as knowledge, technology, and preparation for the future [1].
The Industry 4.0 transition offers many opportunities to reinvent global supply chains with sustainability in mind by significantly improving supply chain processes and achieving strategic results. This digitization drive has become the mainstay of engineering organizations, regardless of size. Industry 4.0 will help organizations achieve sustainable growth and generate higher values in profits and results through faster design and development, innovative products, lower risk and reducing waste to the minimum amount possible. The social point of view reveals that technological modernization is expected to influence the spread of social transformation, especially in developing countries. However, its implementation faces technological and social challenges. It is important to identify contemporary trends and future prospects of sustainable Industry 4.0 and to study more rigorously its impact on sustainable development [2].
The stages in the development of industrial manufacturing systems, from manual labor to the concept of Industry 4.0, can be presented as a path through the four industrial revolutions. This path has been covered in many scientific papers; among them, we can mention [3,4]. The First Industrial Revolution began in the late 18th and early 19th century and was characterized by the introduction of mechanical manufacturing systems, using water and steam. The Second Industrial Revolution began in the late 19th century, symbolized by mass production based on the use of electric power. The Third Industrial Revolution began in the mid-20th century and introduced automation and microelectronic technology in manufacturing. Today, we are in the fourth industrial revolution driven by the development of information and communication technologies (ICT). Its technological basis is the intelligent automation of cyber–physical systems with decentralized control and advanced connectivity (IoT functionalities) [5,6]. The consequence of this new technology for industrial production systems is the reorganization of classical hierarchical automation systems to a self-organizing cyber–physical production system that enables customized and flexible mass production in production quantity [7,8].
The human factor is fundamental in Industry 4.0; its application translates into the “redefinition” of jobs, avoiding isolated knowledge and ensuring interconnectivity, collaboration, and knowledge sharing [9,10]. Therefore, it is necessary to talk about computer supported collaborative work (CSCW) in a way that includes the processes and resources that are involved and offers an integration of tools and methods that support the collaboration of work teams and can potentially improve the productivity and effectiveness of those who work collaboratively [11,12]. CSCW is generally structured around four disciplines: psychological, sociological, organizational and technological. For this paper, we focus only on the technological theme.
We cannot talk about specific technologies, as the concept allows a constant evolution and a continuous integration of new technologies as they mature or adapt, i.e., several emerging technologies are converging to provide more and more digital solutions to the industry, but we will mention the main ones, and their application in this case study [13]. According to the authors of the papers [14,15], they clearly mention the technologies involved in the concept of Industry 4.0. They also propose in the paper [15] a conceptual framework, which they divide into front-end technologies and core technologies. The front-end technologies consider four dimensions (smart manufacturing, smart products, smart supply chain and smart work), while the base technologies consider four (Internet of Things, cloud services, big data and analytics). These works were of great help for our study, as they define a way to analyze the complex and multidisciplinary world of Industry 4.0; however, for our case, we will analyze in terms of paradigms with cyber–physical systems criteria. Our analysis proposal allowed us to clarify the concept and propose better roadmaps toward the fulfillment of each Industry 4.0 project objectives. Finally, the bibliometrics of the research is exposed in the most explicit and graphic way possible in order to not only deliver information to readers, but also as a motivation or inspiration for other researchers to have a vision of the opportunities presented by Industry 4.0 and digital transformation to see how world power countries invest in research in these areas and turn them into technological and economic leaders.

2. Materials and Methods

For the development of this work, we analyzed almost two hundred scientific documents including scientific articles, books, web pages and conference proceedings of the paradigms and main mature and emerging technologies that encompass the concept of Industry 4.0 to ease the understanding of its complexity.
We have tried to include as many of the technologies that are directly or indirectly involved in Industry 4.0 as possible in order to have a fairly broad and objective view of this concept. We mention the databases that were used for the collection of information: MDPI, IEEE, Taylor & Francis, Scopus, IET Inspect, dblp, EBSCO, DOAJ, Springer, Microsoft Academic, ULRICHS WEB, ELSEVIER, ScienceDirect, and Redalyc.
This study follows the systematic review guideline and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to ensure the soundness and reliability of the information used in this work. The main steps taken, including the protocols mentioned above, to validate our research are shown in Figure 1.
The research strategy started with an initial search in the aforementioned databases for scientific information with the following terms or keywords: “Industry 4.0”, “Smart factory”, “Fourth industrial Revolution”, “I4.0” and “Connected Industry 4.0”. The results of this first search allowed us to understand in a general way the concept of Industry 4.0, the studies that were developed on this topic and specifically to identify the main key technologies and paradigms involved in the Fourth Industrial Revolution. The inclusion/exclusion criteria from Table 1 applied were Y01, Y02, Y03, Y04, N01, N02, and N03. In addition, it is worth mentioning that no exclusions were applied due to the region, university or research center, and for this initial search, no restriction was applied with respect to the year of publication.
Once the key technologies and paradigms of Industry 4.0 were identified, the search for scientific information on each of them was individualized with a query as follows: “key technology” and “Industry 4.0”; “key technology” and “Smart factory”; “key technology” and “Fourth industrial Revolution”; “key technology” and “I4.0”, “key technology” and “Connected Industry 4.0”. This type of search was performed to ensure that the article studies the technology in question, but within the concept of Industry 4.0. The inclusion/exclusion criteria applied and present in Table 1 were Y01, Y02, Y05, Y06, Y07, Y08, N01, N02, N03, N04, N05, N06, N07, and N08. It should be noted that there are very few exceptions of articles that did not meet the year inclusion criteria (Y06); however, the content was relevant enough to be included in this study.
Furthermore, this study contains a bibliometric analysis technique to provide details of scientific production through statistical methods. The bibliometric analysis allows us to quantitatively evaluate the articles and recognize measurable patterns of interest, such as the occurrence of keywords, the geographical distribution of articles, year of publication, leading journals and databases. On the other hand, we used a content-centric analysis to identify qualitatively patterns, concepts and interrelation between the paradigms and the key technologies of Industry 4.0.
Finally, this study offers two alternative schemes to analyze the complex concept of Industry 4.0 from a practical perspective to provide industries with a clearer vision of the resources needed for a successful digital transformation.
The search for information for the preparation of this manuscript began in early April 2021, with the aim of presenting some preliminary data at the “XVI International Multidisciplinary Congress of Science and Technology CIT2021” held in June, where it was accepted and successfully presented. After that, the search for specific information intensified until mid-October. The number of articles of each key technology and paradigm found, excluded and included, are mentioned in Table 2.

2.1. Inclusion and Exclusion Criteria

Table 1 shows the inclusion and exclusion criteria used in this study, following the PRISMA protocols and the methodology of a systematic literature review.
The criteria were coded in order to be more easily used and understood in the context of this manuscript. Basically, these criteria allow us to guarantee the trustworthiness of the scientific information, that the sources are rigorous and reliable, and that the information is useful for the purposes of this study.

2.2. Metadata Extraction for Analysis

The strategy applied for the extraction of metadata for subsequent analysis was simple and manual. As an article was included under the inclusion and exclusion criteria, the following data were extracted for each one: geographical origin (country), year of publication, keywords, journal, or scientific publisher where it was published, and language. All this information was stored and classified in Excel spreadsheets for subsequent analysis and elaboration of diagrams. The results of this analysis can be found in the Bibliometric Analysis section.

3. Bibliometric Analysis

All this information allowed us to clarify the evolution of this concept to better abstract it from the academy and to propose strategies for its better analysis and treatment, due to the multidisciplinarity and constant evolution of the concept. This concept is understood as a point of convergence of technologies that is in constant expansion, in parity with technological advances to which new techniques and technologies are progressively annexed.

3.1. Summary of Information Selection

Table 2 summarizes the technologies, number of documents and their respective references analyzed for the elaboration of this work, the graphical representation of the data in this table is shown in Figure 2.

3.2. Geographical Distribution of Scientific Information

In Table 3, we identified the origin of each document used in this work in order to have a reference indicator of the countries that contribute scientifically to this topic. This indicator can give us a brief idea of the direct relationship between scientific research and the industrial development of each country.
A map helps us to visualize geographically all the scientific contributions analyzed. It can be noticed that the United States and China are the countries that do the most research and contribute to this topic; in turn, we can suggest that they are the technologically, industrially, and economically dominant countries. It is shown in Figure 3.
To find how much each country has contributed with its research to the elaboration of this study, each scientific paper was individualized, and its contribution was summed according to its authors and their affiliations. Each different affiliation per article was considered with a value of 1, represented as a percentage in Figure 4.

3.3. Chronology of Scientific Information

Table 4 shows a chronological classification of the scientific papers according to the key technology analyzed. In addition, two schematic diagrams are presented in Figure 5 and Figure 6.

3.4. Keywords Most Frequently Used

A total of 457 different keywords were identified throughout the reference literature. Keywords with the same or very similar definitions or concepts were grouped together to obtain a better analysis of the frequency of occurrence of technologies or concepts within the Industry 4.0 study. In some scientific documents, in which there was no keyword section, nor was this information part of the file metadata, the main words of the title were considered the keywords of the document. Figure 7 shows the 40 most frequently occurring words.

3.5. Contribution Per Journal

For this work, 87 different scientific sources were identified, including journals, books, book chapters and web pages. Figure 8 shows the journals that have contributed the most. The search for articles did not have criteria based on exclusive journals but were selected according to the topics and criteria mentioned in previous sections.

3.6. Language of References

Initially it was stated that the languages accepted would be English and Spanish, with preference always given to English. However, some interesting contributions in Spanish were found and considered. Figure 9 gives the language ratio of the total number of references consulted.

4. Results and Discussion

All the scientific information collected was understood and organized in two schemes, which we propose for an alternative and simple analysis of Industry 4.0.
A qualitative analysis of the information in the references was carried out to obtain the following schemes. These results were obtained by combining a descriptive and interpretative investigation of the references. The main phases of this analysis are as follows:
  • Searching for and obtaining useful information.
  • The preparation, review, and transcription of information.
  • The organization of information and data according to criteria.
  • The categorization, labeling and coding of information and data, which prepares them for analysis.
  • The analysis of the data and the generation of propositions, usefulness, examples, and conclusions.

4.1. Criteria and Paradigms of Industry 4.0 Reorganized

Moving from strategy to execution is the key to an Industry 4.0 implementation study; however, having too much information about all the technologies involved in such a broad concept, this becomes a rather complicated task. Therefore, a poor execution will show us that the strategy, no matter how good it is, will not be successful and the desired objectives will not be achieved. That is why we make a proposal for the organization of I4.0 paradigms, technological knowledge areas; we place certain general aspects based on cyber–physical systems criteria, we highlight the importance of information and data, and finally we have a relationship with the classic pyramid of automation to better abstract the criteria with well-known theoretical concepts.
This schematic in Figure 10 can help inspire and contribute to the design of strategic business plans that can be successfully implemented and align IT, OT and human resources to the execution of these plans. The scheme also serves another purpose, from the point of view of academia, to be analyzed and help the understanding of Industry 4.0 starting from better-known theoretical concepts.
Many general studies talk about the aspects involved in an industry in its digital transformation [1,2,3,4,5,6,7,8,9,10] and within the internal aspects of the value chain we can mention financial resources, social/brand capital, business processes, innovation capabilities, technology, human capital, organizational culture, and management capabilities. However, in this study, we focused on the technological aspects and their respective resources.
This first proposed scheme is based on the automation pyramid, a classification that is widely studied by levels in automation and smart factory issues so that we can start from a theoretical and standardized concept. Below, in our opinion, the areas of knowledge and the general technologies involved are placed in the scheme; this section can help us to plan an appropriate strategy, especially in the management of human resources and related professionals. The areas of knowledge give us a reference of the sciences in charge of the study or development of the key technologies and paradigms that are mentioned at the bottom of the scheme and are vertically aligned to the corresponding area of knowledge. In this way it is easier to identify the appropriate human resource according to the need for key technology that needs to be implemented or developed in a digital transformation of the industry.
Industry 4.0 is characterized by being an integral platform, so there are currently standardized protocols within IIoT of both vertical and horizontal integration that simplify these processes (for example, IO-LINK and MQTT). We can say to better understand that it would be enough a communication protocol to connect IoT devices with each other and at the same time with the cloud directly without the need to go climbing through the automation pyramid as it was analyzed before. That is why the vertical and horizontal integration is not treated with the same criteria as an automation pyramid, simply differentiating the two most notorious levels by aligning the areas of knowledge. We have mentioned the best-known communication protocols. From here on, information plays an important role.
Finally, we reorganized the paradigms of Industry 4.0, according to the areas of knowledge, related to the automation pyramid, and the participation of information within this concept, general aspects of the cyber–physical systems criteria were placed and where each of them would be participating.
In the left column, the key technologies and paradigm were placed, which are in bold and italics. In horizontal correspondence to each paradigm are placed the enabling or support technologies considering the cyber–physical systems criteria, each of these enabling technologies are at the same time vertically aligned with the knowledge areas and the automation pyramid. These enabling or supporting technologies are the more traditional, prevalent and in some cases mature technologies that allow the key technologies or paradigms of Industry 4.0 to perform their intended functions. This section of the scheme lists Industry 4.0 enabling technologies that we have recognized in the included articles. Many of these technologies have been available and commonly used in industry even for decades. In some literature, the prefix “smart” or “intelligent” is often used when naming these technologies, with the understanding that they are developed differently from the ground up to primarily support integration with each other.
The two columns on the right, slightly separated, try to explain the role of data in Industry 4.0. It starts from the vertical and horizontal integration, where its main function is the transmission of data, then briefly analyzes each key technology or paradigm to identify the role that each of them have in the management of both local data and in the cloud. These data and information management are one of the main characteristics of Industry 4.0, and practically intervene in almost all the paradigms analyzed; they could be considered core technologies of Industry 4.0 because they are an essential part of the integration between enabling technologies, key technologies and paradigms. Moreover, the treatment of this information and data in each paradigm is the basis of the Industry 4.0 concept.
Obviously not all technologies and criteria are present, and there will always be the possibility of integrating new technologies or new paradigms appearing. However, the proposed scheme is intended as a guide and reference to abstract in the best possible way the complex concept of Industry 4.0.
To better understand the scheme, we analyze some key technologies. Cybersecurity is a very important paradigm that is present at all levels of automation. Especially in the protection of data, both local and in the cloud, we can mention at lower levels that the role of cybersecurity is to ensure the integrity of the device and the integrity of the data obtained; the following levels must mainly ensure secure access to data and secure data transmissions, finally reaching the exclusive protection of local data and secure cloud storage. This key technology, as can be seen, is involved in all levels of Industry 4.0, and we can corroborate this by knowing that the core of the digitization of industry is the management of data at all levels, and these must always be protected.
Another key technology is cloud computing. This technology, despite being very important within Industry 4.0, is exclusively involved in data collection, data transmission to the cloud, and data computation in the cloud; in other words, it is not present at all levels.
IoT and IIoT have functions that are present at all levels of both automation and data, require professionals from all technological areas and are mentioned in the scheme. This undoubtedly ratifies the importance of this key technology in Industry 4.0; it is the fundamental pillar that supports, even with its architecture, the basis of cyber–physical systems.

4.2. CSCW Matrix for Industry 4.0 Paradigms

Our proposal stems from the initial difficulty of being able to place an Industry 4.0 paradigm in a single specific quadrant of the classic CSCW matrix, obviously because the CSCW concept is very useful but relatively old with respect to these emerging technologies. The matrix has had certain modifications to adapt as best as possible to the collaborative participation of each paradigm analyzed.
The purpose of this classification is to understand that these new concepts are not so much spatially or temporally located in a single quadrant, but that their scope goes beyond these classic boundaries and that they are collaborative and interdependent. For example, augmented reality interacts directly with the plant and machinery (same place) but requires high computational capacity and processing speed that is done with cloud computing (different place) all working in real time (same time), being a paradigm that is difficult to place in a classic automation pyramid or a classic CSCW matrix. However, there are also paradigms that can be placed in a single quadrant, as is the case of edge computing, which is characterized by performing computing and data storage in the location where it is needed (same place) to improve response times (same time) and save bandwidth.
Additionally, we considered adding a level to the place axis. Initially, in a CSCW matrix, there are only “same place” and “different place”, but in some paradigms, they can be interpreted to work in intermediate places, which we call “near place”, as is the case of fog Computing, which is part of a decentralized architecture where an additional edge processing unit is used to perform a substantial amount of computation, storage and communication locally not precisely on the device, but as an aid to cloud computing because it reduces the traffic between IoT devices and the cloud, i.e., for data to be processed locally and devices to communicate with each other, an additional processing unit has to be added to the IoT devices.
Subsequently, the rest of the paradigms were placed according to their functions. Figure 11 shows a CSCW matrix adapted to the Industry 4.0 concept.

5. Conclusions

The proposed Industry 4.0 paradigm organization schemes will help academia and the industrial sector with little experience to propose more appropriate strategies according to the needs, available resources, and resources that will be required to ensure a successful implementation.
There is always the possibility that new technologies, new criteria, and new paradigms can be integrated into the proposed schemes, or even be modified due to the different opinions of other professionals in certain technical aspects, because Industry 4.0 is a rather broad topic, and it is also constantly evolving along with the rapid technological progress.
Moving from strategic planning to successful execution requires not only a correct digital maturity assessment and a proper roadmap, but also the proper management of resources: IT, OT, and human resources. Being as clear as possible about the complex concept of Industry 4.0 and its paradigms is very important for human resource management and related professionals to implement the successful execution of these plans.
The proposed schemes are based on an exhaustive literature search, but also on criteria and experience from an ongoing project based on the needs and problems that have to be faced; therefore, relatively different criteria may exist. The main purpose is to be a classification basis to better understand the Industry 4.0 concept.
The large amount of scientific information on these topics is a limiting factor for a more complete analysis, so there are still technologies that were not mentioned and that can be included in this proposed scheme. However, the main ones are found in the schemes.
Global market conditions have been strongly influenced by the COVID-19 pandemic and have led us to understand the importance of technology in the industry. This has led many companies to envision an early digital transformation, which will require as much information as possible to assist in the execution of their strategies.
Industry 4.0 paradigms have a spatial and temporal distribution that do not fit in a classical CSCW matrix, so it is necessary to adapt a matrix to best represent this new form of distribution. This is due to the integration of several technologies that become more complex concepts and difficult to be abstracted in a traditional way.
Investment in research on these topics has a direct influence on the economy of each country; countries that do more research on these topics are those that are more advanced industrially, and it does not seem to be a casual coincidence of this direct relationship of the documents analyzed in this study. Therefore, it is necessary to encourage research in sectors with developing economies.
Today, companies are strongly considering the option of digital transformation and entry into Industry 4.0, and thus, the scope, and technology trends for sustainable results. Industry 4.0 offers different solutions for sustainability, including contributing to social sustainability that provides a good working environment that is safer and more pleasant for workers.
In spite of being a widely studied topic and having a lot of scientific information, the amount of information is not enough to understand the interrelation of the technologies that integrate the concept of Industry 4.0, which became an obstacle at the moment of applying the concepts and above all for taking advantage of the available human resources. There is some conflict in the scope of each professional in different areas of knowledge, as well as the limitations of the same professionals. For example, professionals in the areas of IT and ICT experts in developing software applications, KPIs, APIs, etc., have limited knowledge in areas of electronics and automation; the same happens in the opposite case, that is, it would be impossible to work with only one type of professional. It is even difficult to work with each professional needed if they do not know their scope and limitations. That is why investigating the interrelationship of key technologies and paradigms of Industry 4.0, relating both areas of knowledge, levels of automation, information management, communications, with each paradigm and key technology, and representing them in a scheme was a necessity that now allows us to have a theoretical tool to continue with future research accompanied by real implementations in the industry.

Author Contributions

Conceptualization, C.R.-J. and R.G.C.; methodology, C.R.-J. and R.G.C.; validation, R.G.C. and F.M.C.; formal analysis, C.R.-J. and R.G.C.; investigation, C.R.-J. and R.G.C.; resources, R.G.C. and F.M.C.; data curation, C.R.-J.; writing—original draft preparation, C.R.-J. and E.G.-C.; writing—review and editing, C.R.-J. and R.G.C.; visualization, R.G.C. and E.H.F.; supervision, R.G.C., F.M.C. and E.H.F.; project administration, R.G.C.; funding acquisition, R.G.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially funded by AEI/FEDER, grant number PID2019-107258RB-C31 and by IDEA/FEDER grant OFFSHOREWIND with number 802C2000003.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. IMD World Digital. IMD World Digital Competitiveness Ranking 2021. IMD World Compet. Available online: https://www.imd.org/globalassets/wcc/docs/release-2021/digital_2021.pdf (accessed on 29 September 2021).
  2. Narula, S.; Puppala, H.; Kumar, A.; Frederico, G.F.; Dwivedy, M.; Prakash, S.; Talwar, V. Applicability of industry 4.0 technologies in the adoption of global reporting initiative standards for achieving sustainability. J. Clean Prod. 2021, 305, 127141. [Google Scholar] [CrossRef]
  3. Barros, T.; Muñuzuri, J. La industria 4.0: Aplicaciones e Implicaciones. Master’s Thesis, The University of Seville, Seville, Spain, 2017; pp. 1–52. [Google Scholar]
  4. Bartodziej, C.J. Technologies and functions of the concept Industry 4.0. In The Concept Industry 4.0: An Empirical Analysis of Technologies and Applications in Production Logistics; Springer Fachmedien Wiesbaden: Wiesbaden, Germany, 2017; pp. 51–78. ISBN 978-3-658-16502-4. [Google Scholar]
  5. Hermann, M.; Pentek, T.; Otto, B. Design Principles for Industrie 4.0 Scenarios. In Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, 5–8 January 2016; pp. 3928–3937. [Google Scholar]
  6. Moeuf, A.; Pellerin, R.; Lamouri, S.; Tamayo-Giraldo, S.; Barbaray, R. The industrial management of SMEs in the era of Industry 4.0. Int. J. Prod. Res. 2018, 56, 1118–1136. [Google Scholar] [CrossRef] [Green Version]
  7. Rojko, A. Industry 4.0 concept: Background and overview. Int. J. Interact. Mob. Technol. 2017, 11, 77–90. [Google Scholar] [CrossRef] [Green Version]
  8. Xu, L.D.; Xu, E.L.; Li, L. Industry 4.0: State of the art and future trends. Int. J. Prod. Res. 2018, 56, 7543. [Google Scholar] [CrossRef] [Green Version]
  9. Stachová, K.; Papula, J.; Stacho, Z.; Kohnová, L. External partnerships in employee education and development as the key to facing industry 4.0 challenges. Sustainability 2019, 11, 345. [Google Scholar] [CrossRef] [Green Version]
  10. Ignacio, J.; Osma, P.; Leandro, F.; Salazar, M.; Natalia, K.; Gómez, M. Knowledge Management and Industry 4.0 and Open Innovation. Rev. Ing. Solidar. 2020, 16, 2. [Google Scholar] [CrossRef]
  11. Yun, J.J.; Liu, Z. Micro- and Macro-Dynamics of Open Innovation with a Quadruple-Helix Model. Sustainability 2019, 11, 3301. [Google Scholar] [CrossRef] [Green Version]
  12. Vila, C.; Ugarte, D.; Ríos, J.; Abellán, J.V. Project-based collaborative engineering learning to develop Industry 4.0 skills within a PLM framework. Procedia Manuf. 2017, 13, 1269–1276. [Google Scholar] [CrossRef]
  13. Ghobakhloo, M.; Fathi, M.; Iranmanesh, M.; Maroufkhani, P.; Morales, M.E. Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants. J. Clean. Prod. 2021, 302, 127052. [Google Scholar] [CrossRef]
  14. Dalenogare, L.S.; Benitez, G.B.; Ayala, N.F.; Frank, A.G. The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 2018, 204, 383–394. [Google Scholar] [CrossRef]
  15. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019, 210, 15–26. [Google Scholar] [CrossRef]
  16. O’Donovan, P.; Gallagher, C.; Bruton, K.; O’Sullivan, D.T.J. A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications. Manuf. Lett. 2018, 15, 139–142. [Google Scholar] [CrossRef]
  17. Lee, J.; Bagheri, B.; Kao, H.A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. [Google Scholar] [CrossRef]
  18. Fernández-Caramés, T.M.; Fraga-Lamas, P.; Suárez-Albela, M.; Díaz-Bouza, M.A. A fog computing based cyber-physical system for the automation of pipe-related tasks in the industry 4.0 shipyard. Sensors 2018, 18, 1961. [Google Scholar] [CrossRef] [Green Version]
  19. Ramirez, F.I.J.; Barrionuevo, J.M.J. Cyber-physical system for quality control of spur gears through artificial vision techniques. In Proceedings of the 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador, 11–15 November 2019; pp. 3–8. [Google Scholar] [CrossRef]
  20. Kim, S.; Park, S. CPS(Cyber Physical System) based Manufacturing System Optimization. Procedia Comput. Sci. 2017, 122, 518–524. [Google Scholar] [CrossRef]
  21. O’Donovan, P.; Gallagher, C.; Leahy, K.; O’Sullivan, D.T.J. A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications. Comput. Ind. 2019, 110, 12–35. [Google Scholar] [CrossRef]
  22. Ajayi, O.J.; Rafferty, J.; Santos, J.; Garcia-Constantino, M.; Cui, Z. BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems. IoT 2021, 2, 610–632. [Google Scholar] [CrossRef]
  23. Mouromtsev, D. Semantic Reference Model for Individualization of Information Processes in IoT Heterogeneous Environment. Electronics 2021, 10, 2523. [Google Scholar] [CrossRef]
  24. Mecca, G.; Santomauro, M.; Santoro, D.; Veltri, E. IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures. Appl. Sci. 2021, 11, 9670. [Google Scholar] [CrossRef]
  25. França, C.M.; Couto, R.S.; Velloso, P.B. Missing Data Imputation in Internet of Things Gateways. Information 2021, 12, 425. [Google Scholar] [CrossRef]
  26. Yuan, C.; Wang, C.-C.; Chang, M.-L.; Lin, W.-T.; Lin, P.-A.; Lee, C.-C.; Tsui, Z.-L. Using a Flexible IoT Architecture and Sequential AI Model to Recognize and Predict the Production Activities in the Labor-Intensive Manufacturing Site. Electronics 2021, 10, 2540. [Google Scholar] [CrossRef]
  27. Wytrębowicz, J.; Cabaj, K.; Krawiec, J. Messaging Protocols for IoT Systems—A Pragmatic Comparison. Sensors 2021, 21, 6904. [Google Scholar] [CrossRef]
  28. Goworko, M.; Wytrębowicz, J. A Secure Communication System for Constrained IoT Devices—Experiences and Recommendations. Sensors 2021, 21, 6906. [Google Scholar] [CrossRef] [PubMed]
  29. Luong, N.C.; Hoang, D.T.; Wang, P.; Niyato, D.; Kim, D.I.; Han, Z. Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey. IEEE Commun. Surv. Tutor. 2016, 18, 2546–2590. [Google Scholar] [CrossRef] [Green Version]
  30. Medina, C.A.; Pérez, M.R.; Trujillo, L.C. IoT Paradigm into the Smart City Vision: A Survey. In Proceedings of the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, UK, 21–23 June 2017; pp. 695–704. [Google Scholar]
  31. Xu, L.D.; He, W.; Li, S. Internet of Things in Industries: A Survey. IEEE Trans. Ind. Inform. 2014, 10, 2233–2243. [Google Scholar] [CrossRef]
  32. Jeschke, S.; Brecher, C.; Meisen, T.; Özdemir, D.; Eschert, T. Industrial Internet of Things and Cyber Manufacturing Systems. In Industrial Internet of Things: Cybermanufacturing Systems; Jeschke, S., Brecher, C., Song, H., Rawat, D.B., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 3–19. ISBN 978-3-319-42559-7. [Google Scholar]
  33. Gartner. Leading the IoT—Gartner Insights on How to Lead in a Connected World; Gartner: Stamford, CT, USA, 2017; ISBN 0004-0002. Available online: https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf (accessed on 12 February 2021).
  34. Ganchev, I.; Ji, Z.; O’Droma, M. A generic IoT architecture for smart cities. In Proceedings of the 25th IET Irish Signals Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies (ISSC 2014/CIICT 2014), Limerick, Ireland, 26–27 June 2014; pp. 196–199. [Google Scholar]
  35. Grønbæk, I. Architecture for the Internet of Things (IoT): API and Interconnect. In Proceedings of the 2008 Second International Conference on Sensor Technologies and Applications (Sensorcomm 2008), Cap Esterel, France, 25–31 August 2008; pp. 802–807. [Google Scholar]
  36. Munirathinam, S. Industry 4.0: Industrial Internet of Things (IIOT), 1st ed.; Elsevier Inc.: Amsterdam, The Netherlands, 2020; Volume 117, ISBN 9780128187562. [Google Scholar]
  37. Industrial Internet of Things (IIoT)—Definition—Trend Micro USA. Available online: https://www.trendmicro.com/vinfo/us/security/definition/industrial-internet-of-things-iiot (accessed on 6 November 2020).
  38. Madakam, S.; Ramaswamy, R.; Tripathi, S. Internet of Things (IoT): A Literature Review. J. Comput. Commun. 2015, 3, 164–173. [Google Scholar] [CrossRef] [Green Version]
  39. Corallo, A.; Lazoi, M.; Lezzi, M. Cybersecurity in the context of industry 4.0: A structured classification of critical assets and business impacts. Comput. Ind. 2020, 114, 103165. [Google Scholar] [CrossRef]
  40. Lu, Y.; Xu, L. Da Internet of things (IoT) cybersecurity research: A review of current research topics. IEEE Internet Things J. 2019, 6, 2103–2115. [Google Scholar] [CrossRef]
  41. Bordel, B.; Alcarria, R.; Sánchez-de-Rivera, D.; Robles, T. Protecting industry 4.0 systems against the malicious effects of cyber-physical attacks. In International Conference on Ubiquitous Computing and Ambient Intelligence; Springer: Berlin/Heidelberg, Germany, 2017; pp. 161–171. [Google Scholar] [CrossRef]
  42. Ashibani, Y.; Mahmoud, Q.H. Cyber physical systems security: Analysis, challenges and solutions. Comput. Secur. 2017, 68, 81–97. [Google Scholar] [CrossRef]
  43. Xu, P.; He, S.; Wang, W.; Susilo, W.; Jin, H. Lightweight searchable public-key encryption for cloud-assisted wireless sensor networks. IEEE Trans. Ind. Inform. 2018, 14, 3712–3723. [Google Scholar] [CrossRef]
  44. Khalid, A.; Kirisci, P.; Khan, Z.H.; Ghrairi, Z.; Thoben, K.D.; Pannek, J. Security framework for industrial collaborative robotic cyber-physical systems. Comput. Ind. 2018, 97, 132–145. [Google Scholar] [CrossRef] [Green Version]
  45. Lezzi, M.; Lazoi, M.; Corallo, A. Cybersecurity for Industry 4.0 in the current literature: A reference framework. Comput. Ind. 2018, 103, 97–110. [Google Scholar] [CrossRef]
  46. Urquhart, L.; McAuley, D. Avoiding the internet of insecure industrial things. Comput. Law Secur. Rev. 2018, 34, 450–466. [Google Scholar] [CrossRef]
  47. Lee, I. Cybersecurity: Risk management framework and investment cost analysis. Bus. Horiz. 2021, 64, 659–671. [Google Scholar] [CrossRef]
  48. Yazdinejad, A.; Zolfaghari, B.; Azmoodeh, A.; Dehghantanha, A.; Karimipour, H.; Fraser, E.; Green, A.G.; Russell, C.; Duncan, E. A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures. Appl. Sci. 2021, 11, 7518. [Google Scholar] [CrossRef]
  49. Paredes, C.M.; Martínez-Castro, D.; Ibarra-Junquera, V.; González-Potes, A. Detection and Isolation of DoS and Integrity Cyber Attacks in Cyber-Physical Systems with a Neural Network-Based Architecture. Electronics 2021, 10, 2238. [Google Scholar] [CrossRef]
  50. Yang, W.; Wang, S.; Sahri, N.M.; Karie, N.M.; Ahmed, M.; Valli, C. Biometrics for Internet-of-Things Security: A Review. Sensors 2021, 21, 6163. [Google Scholar] [CrossRef] [PubMed]
  51. Krause, T.; Ernst, R.; Klaer, B.; Hacker, I.; Henze, M. Cybersecurity in Power Grids: Challenges and Opportunities. Sensors 2021, 21, 6225. [Google Scholar] [CrossRef] [PubMed]
  52. Northern, B.; Burks, T.; Hatcher, M.; Rogers, M.; Ulybyshev, D. VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems. Information 2021, 12, 408. [Google Scholar] [CrossRef]
  53. Oussous, A.; Benjelloun, F.Z.; Ait Lahcen, A.; Belfkih, S. Big Data technologies: A survey. J. King Saud Univ.—Comput. Inf. Sci. 2018, 30, 431–448. [Google Scholar] [CrossRef]
  54. Coda, F.A.; Salles, R.M.D.; Junqueira, F.; Filho, D.J.S.; Silva, J.R.; Miyagi, P.E. Big data systems requirements for Industry 4.0. In Proceedings of the 2018 13th IEEE International Conference on Industry Applications (INDUSCON), Sao Paulo, Brazil, 12–14 November 2018; pp. 1230–1236. [Google Scholar] [CrossRef]
  55. Yin, S.; Kaynak, O. Big Data for Modern Industry: Challenges and Trends. Proc. IEEE 2015, 103, 143–146. [Google Scholar] [CrossRef]
  56. Yan, J.; Meng, Y.; Lu, L.; Li, L. Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance. IEEE Access 2017, 5, 23484–23491. [Google Scholar] [CrossRef]
  57. Shobana, V.; Kumar, N. Big data—A review. Int. J. Appl. Eng. Res. 2015, 10, 1294–1298. [Google Scholar]
  58. Philip Chen, C.L.; Zhang, C.Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Inf. Sci. 2014, 275, 314–347. [Google Scholar] [CrossRef]
  59. Lopez-Miguel, I.D. Survey on Preprocessing Techniques for Big Data Projects. Eng. Proc. 2021, 7, 14. [Google Scholar] [CrossRef]
  60. Lafuente-Lechuga, M.; Cifuentes-Faura, J.; Faura-Martínez, U. Sustainability, Big Data and Mathematical Techniques: A Bibliometric Review. Mathematics 2021, 9, 2557. [Google Scholar] [CrossRef]
  61. Alhameli, F.; Ahmadian, A.; Elkamel, A. Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub. Energies 2021, 14, 6682. [Google Scholar] [CrossRef]
  62. Pilloni, V. How data will transform industrial processes: Crowdsensing, crowdsourcing and big data as pillars of industry 4.0. Future Internet 2018, 10, 24. [Google Scholar] [CrossRef] [Green Version]
  63. Reis, M.S.; Gins, G. Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis. Processes 2017, 5, 35. [Google Scholar] [CrossRef] [Green Version]
  64. Kostakis, P.; Kargas, A. Big-Data Management: A Driver for Digital Transformation? Information 2021, 12, 411. [Google Scholar] [CrossRef]
  65. Cofre-Martel, S.; Droguett, E.L.; Modarres, M. Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management. Sensors 2021, 21, 6841. [Google Scholar] [CrossRef]
  66. Oprea, S.-V.; Bâra, A.; Puican, F.C.; Radu, I.C. Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption. Sustainability 2021, 13, 10963. [Google Scholar] [CrossRef]
  67. Ge, Z.; Song, Z.; Gao, F. Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res. 2013, 52, 3543–3562. [Google Scholar] [CrossRef]
  68. Weese, M.; Martinez, W.; Megahed, F.M.; Jones-Farmer, L.A. Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective. J. Qual. Technol. 2016, 48, 4–24. [Google Scholar] [CrossRef]
  69. Qin, S.J. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control 2012, 36, 220–234. [Google Scholar] [CrossRef]
  70. Xu, L.D.; Duan, L. Big data for cyber physical systems in industry 4.0: A survey. Enterp. Inf. Syst. 2019, 13, 148–169. [Google Scholar] [CrossRef]
  71. Lidong, W.; Guanghui, W. Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0. Int. J. Eng. Manuf. 2016, 6, 1–8. [Google Scholar] [CrossRef] [Green Version]
  72. Khan, M.; Wu, X.; Xu, X.; Dou, W. Big data challenges and opportunities in the hype of Industry 4.0. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6. [Google Scholar] [CrossRef]
  73. Abbas, H.; Shaheen, S. Future SCADA challenges and the promising solution: The agent-based SCADA. Int. J. Crit. Infrastruct. 2014, 10, 307–333. [Google Scholar] [CrossRef]
  74. Li, G.; Tan, J.; Chaudhry, S.S. Industry 4.0 and big data innovations. Enterp. Inf. Syst. 2019, 13, 145–147. [Google Scholar] [CrossRef] [Green Version]
  75. Qi, Q.; Tao, F. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access 2018, 6, 3585–3593. [Google Scholar] [CrossRef]
  76. Tao, F.; Cheng, J.; Qi, Q.; Zhang, M.; Zhang, H.; Sui, F. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 2018, 94, 3563–3576. [Google Scholar] [CrossRef]
  77. Yildiz, E.; Møller, C.; Bilberg, A. Virtual Factory: Digital Twin Based Integrated Factory Simulations. Procedia CIRP 2020, 93, 216–221. [Google Scholar] [CrossRef]
  78. Garg, G.; Kuts, V.; Anbarjafari, G. Digital Twin for FANUC Robots: Industrial Robot Programming and Simulation Using Virtual Reality. Sustainability 2021, 13, 10336. [Google Scholar] [CrossRef]
  79. Augustyn, D.; Ulriksen, M.D.; Sørensen, J.D. Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information. Energies 2021, 14, 5859. [Google Scholar] [CrossRef]
  80. Falekas, G.; Karlis, A. Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects. Energies 2021, 14, 5933. [Google Scholar] [CrossRef]
  81. Huang, Z.; Shen, Y.; Li, J.; Fey, M.; Brecher, C. A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors 2021, 21, 6340. [Google Scholar] [CrossRef] [PubMed]
  82. Zhang, Z.; Zou, Y.; Zhou, T.; Zhang, X.; Xu, Z. Energy Consumption Prediction of Electric Vehicles Based on Digital Twin Technology. World Electr. Veh. J. 2021, 12, 160. [Google Scholar] [CrossRef]
  83. Huo, Y.; Yang, A.; Jia, Q.; Chen, Y.; He, B.; Li, J. Efficient Visualization of Large-Scale Oblique Photogrammetry Models in Unreal Engine. ISPRS Int. J. Geo-Inform. 2021, 10, 643. [Google Scholar] [CrossRef]
  84. Vidal-Balea, A.; Blanco-Novoa, O.; Fraga-Lamas, P.; Vilar-Montesinos, M.; Fernández-Caramés, T.M. Collaborative Augmented Digital Twin: A Novel Open-Source Augmented Reality Solution for Training and Maintenance Processes in the Shipyard of the Future. Eng. Proc. 2021, 7, 10. [Google Scholar] [CrossRef]
  85. Yasin, A.; Pang, T.Y.; Cheng, C.-T.; Miletic, M. A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises. Appl. Sci. 2021, 11, 9479. [Google Scholar] [CrossRef]
  86. Tu, X.; Autiosalo, J.; Jadid, A.; Tammi, K.; Klinker, G. A Mixed Reality Interface for a Digital Twin Based Crane. Appl. Sci. 2021, 11, 9480. [Google Scholar] [CrossRef]
  87. Fathy, Y.; Jaber, M.; Nadeem, Z. Digital Twin-Driven Decision Making and Planning for Energy Consumption. J. Sens. Actuator Netw. 2021, 10, 37. [Google Scholar] [CrossRef]
  88. Hänel, A.; Seidel, A.; Frieß, U.; Teicher, U.; Wiemer, H.; Wang, D.; Wenkler, E.; Penter, L.; Hellmich, A.; Ihlenfeldt, S. Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach. J. Manuf. Mater. Process. 2021, 5, 80. [Google Scholar] [CrossRef]
  89. Nee, A.Y.C.; Ong, S.K. Special Issue on Digital Twins in Industry. Appl. Sci. 2021, 11, 6437. [Google Scholar] [CrossRef]
  90. Tao, F.; Zhang, M. Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing. IEEE Access 2017, 5, 20418–20427. [Google Scholar] [CrossRef]
  91. Velásquez, N.; Estevez, E.; Pesado, P. Cloud Computing, Big Data and the Industry 4.0 Reference Architectures. J. Comput. Sci. Technol. 2018, 18, e29. [Google Scholar] [CrossRef]
  92. Aceto, G.; Persico, V.; Pescapé, A. Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0. J. Ind. Inf. Integr. 2020, 18, 100129. [Google Scholar] [CrossRef]
  93. Fernández-Caramés, T.M.; Fraga-Lamas, P.; Suárez-Albela, M.; Vilar-Montesinos, M. A fog computing and cloudlet based augmented reality system for the industry 4.0 shipyard. Sensors 2018, 18, 1798. [Google Scholar] [CrossRef] [Green Version]
  94. Aazam, M.; Zeadally, S.; Harras, K.A. Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Trans. Ind. Inform. 2018, 14, 4674–4682. [Google Scholar] [CrossRef]
  95. Fog Computing: La Nube se Prepara para el Internet de las Cosas—IONOS. Available online: https://www.ionos.es/digitalguide/servidores/know-how/fog-computing/ (accessed on 15 August 2021).
  96. Botta, A.; De Donato, W.; Persico, V.; Pescapé, A. Integration of Cloud computing and Internet of Things: A survey. Future Gener. Comput. Syst. 2016, 56, 684–700. [Google Scholar] [CrossRef]
  97. Yang, C.; Lan, S.; Shen, W.; Wang, L.; Huang, G.Q. Software-defined Cloud Manufacturing with Edge Computing for Industry 4.0. In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15–19 June 2020; pp. 1618–1623. [Google Scholar] [CrossRef]
  98. Sittón-Candanedo, I.; Alonso, R.S.; Rodríguez-González, S.; García Coria, J.A.; De La Prieta, F. Edge Computing Architectures in Industry 4.0: A General Survey and Comparison. In Proceedings of the 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019), Seville, Spain, 13–15 May 2020; Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J.A., Quintián, H., Corchado, E., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 121–131. [Google Scholar]
  99. Vaquero, L.M.; Rodero-Merino, L. Finding Your Way in the Fog: Towards a Comprehensive Definition of Fog Computing. SIGCOMM Comput. Commun. Rev. 2014, 44, 27–32. [Google Scholar] [CrossRef]
  100. Trinks, S. Edge Computing architecture to support Real Time Analytic applications. In Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10–13 December 2018; pp. 2930–2939. [Google Scholar]
  101. Zhou, X.; Ke, R.; Yang, H.; Liu, C. When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges. Appl. Sci. 2021, 11, 9680. [Google Scholar] [CrossRef]
  102. Kjorveziroski, V.; Filiposka, S.; Trajkovik, V. IoT Serverless Computing at the Edge: A Systematic Mapping Review. Computers 2021, 10, 130. [Google Scholar] [CrossRef]
  103. Peyman, M.; Copado, P.J.; Tordecilla, R.D.; Martins, L.d.C.; Xhafa, F.; Juan, A.A. Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems. Energies 2021, 14, 6309. [Google Scholar] [CrossRef]
  104. Xu, R.; Hang, L.; Jin, W.; Kim, D. Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments. Actuators 2021, 10, 197. [Google Scholar] [CrossRef]
  105. Stan, O.P.; Enyedi, S.; Corches, C.; Flonta, S.; Stefan, I.; Gota, D.; Miclea, L. Method to Increase Dependability in a Cloud-Fog-Edge Environment. Sensors 2021, 21, 4714. [Google Scholar] [CrossRef] [PubMed]
  106. Prakash, V.; Williams, A.; Garg, L.; Savaglio, C.; Bawa, S. Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems. Electronics 2021, 10, 1229. [Google Scholar] [CrossRef]
  107. Jalowiczor, J.; Rozhon, J.; Voznak, M. Study of the Efficiency of Fog Computing in an Optimized LoRaWAN Cloud Architecture. Sensors 2021, 21, 3159. [Google Scholar] [CrossRef] [PubMed]
  108. Kim, T.; Yoo, S.; Kim, Y. Edge/Fog Computing Technologies for IoT Infrastructure. Sensors 2021, 21, 3001. [Google Scholar] [CrossRef]
  109. Wang, X.; Wan, J. Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources. Appl. Sci. 2021, 11, 3188. [Google Scholar] [CrossRef]
  110. Mijuskovic, A.; Chiumento, A.; Bemthuis, R.; Aldea, A.; Havinga, P. Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification. Sensors 2021, 21, 1832. [Google Scholar] [CrossRef]
  111. Hamdan, S.; Ayyash, M.; Almajali, S. Edge-Computing Architectures for Internet of Things Applications: A Survey. Sensors 2020, 20, 6441. [Google Scholar] [CrossRef]
  112. Lee, J.; Lee, K.; Yoo, A.; Moon, C. Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles. Electronics 2020, 9, 2084. [Google Scholar] [CrossRef]
  113. Chegini, H.; Naha, R.K.; Mahanti, A.; Thulasiraman, P. Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy. IoT 2021, 2, 92–118. [Google Scholar] [CrossRef]
  114. Choi, J.; Ahn, S. Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment. Sensors 2021, 21, 1506. [Google Scholar] [CrossRef] [PubMed]
  115. Almutairi, J.; Aldossary, M. Modeling and Analyzing Offloading Strategies of IoT Applications over Edge Computing and Joint Clouds. Symmetry 2021, 13, 402. [Google Scholar] [CrossRef]
  116. Next Generation Mobile Networks Alliance 5G Initiative 5G White Paper. 2015. Available online: https://www.ngmn.org/work-programme/5g-white-paper.html (accessed on 6 November 2020).
  117. Wang, K.; Yu, F.R.; Li, H. Information-Centric Virtualized Cellular Networks With Device-to-Device Communications. IEEE Trans. Veh. Technol. 2016, 65, 9319–9329. [Google Scholar] [CrossRef]
  118. Muller, M.; Behnke, D.; Bok, P.B.; Peuster, M.; Schneider, S.; Karl, H. 5G as key technology for networked factories: Application of vertical-specific network services for enabling flexible smart manufacturing. In Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 22–25 July 2019; pp. 1495–1500. [Google Scholar] [CrossRef]
  119. Doppler, K.; Rinne, M.; Wijting, C.; Ribeiro, C.B.; Hugl, K. Device-to-device communication as an underlay to LTE-advanced networks. IEEE Commun. Mag. 2009, 47, 42–49. [Google Scholar] [CrossRef]
  120. Rodriguez, I.; Mogensen, R.S.; Fink, A.; Raunholt, T.; Markussen, S.; Christensen, P.H.; Berardinelli, G.; Mogensen, P.; Schou, C.; Madsen, O. An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications. Energies 2021, 14, 4444. [Google Scholar] [CrossRef]
  121. Silva, M.M.D.; Guerreiro, J. On the 5G and Beyond. Appl. Sci. 2020, 10, 7091. [Google Scholar] [CrossRef]
  122. Fanibhare, V.; Sarkar, N.I.; Al-Anbuky, A. A Survey of the Tactile Internet: Design Issues and Challenges, Applications, and Future Directions. Electronics 2021, 10, 2171. [Google Scholar] [CrossRef]
  123. Varga, P.; Peto, J.; Franko, A.; Balla, D.; Haja, D.; Janky, F.; Soos, G.; Ficzere, D.; Maliosz, M.; Toka, L. 5G support for Industrial IoT Applications—Challenges, Solutions, and Research gaps. Sensors 2020, 20, 828. [Google Scholar] [CrossRef] [Green Version]
  124. Khatib, E.J.; Barco, R. Optimization of 5G Networks for Smart Logistics. Energies 2021, 14, 1758. [Google Scholar] [CrossRef]
  125. Segura, D.; Khatib, E.J.; Munilla, J.; Barco, R. 5G Numerologies Assessment for URLLC in Industrial Communications. Sensors 2021, 21, 2489. [Google Scholar] [CrossRef]
  126. Alkinani, M.H.; Almazroi, A.A.; Jhanjhi, N.Z.; Khan, N.A. 5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle. Sensors 2021, 21, 6905. [Google Scholar] [CrossRef]
  127. Gu, X.; Zhu, M.; Zhuang, L. Highly Efficient Spatial–Temporal Correlation Basis for 5G IoT Networks. Sensors 2021, 21, 6899. [Google Scholar] [CrossRef] [PubMed]
  128. Kropp, A.; Schmoll, R.S.; Nguyen, G.T.; Fitzek, F.H.P. Demonstration of a 5G Multi-access Edge Cloud Enabled Smart Sorting Machine for Industry 4.0. In Proceedings of the 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 11–14 January 2019. [Google Scholar] [CrossRef]
  129. Shi, Y.; Han, Q.; Shen, W.; Zhang, H. Potential applications of 5G communication technologies in collaborative intelligent manufacturing. IET Collab. Intell. Manuf. 2019, 1, 109–116. [Google Scholar] [CrossRef]
  130. Ambika, P. Machine learning and deep learning algorithms on the Industrial Internet of Things (IIoT). In Advances in Computers; Elsevier Inc.: Amsterdam, The Netherlands, 2020; Volume 117, pp. 321–338. ISBN 9780128187562. [Google Scholar]
  131. Data Clustering Algorithms—k-Means Clustering Algorithm. Available online: https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm (accessed on 16 November 2020).
  132. Deng, S.; Zhao, H.; Fang, W.; Yin, J.; Dustdar, S.; Zomaya, A.Y. Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. IEEE Internet Things J. 2020, 7, 7457–7469. [Google Scholar] [CrossRef] [Green Version]
  133. Singh, R.; Sharma, R.; Vaseem Akram, S.; Gehlot, A.; Buddhi, D.; Malik, P.K.; Arya, R. Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning. Saf. Sci. 2021, 143, 105407. [Google Scholar] [CrossRef]
  134. Parto, M.; Saldana, C.; Kurfess, T. A Novel Three-Layer IoT Architecture for Shared, Private, Scalable, and Real-time Machine Learning from Ubiquitous Cyber-Physical Systems. Procedia Manuf. 2020, 48, 959–967. [Google Scholar] [CrossRef]
  135. Lee, J.; Davari, H.; Singh, J.; Pandhare, V. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 2018, 18, 20–23. [Google Scholar] [CrossRef]
  136. Ayvaz, S.; Alpay, K. Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Syst. Appl. 2021, 173, 114598. [Google Scholar] [CrossRef]
  137. Ansari, F.; Erol, S.; Sihn, W. Rethinking Human-Machine Learning in Industry 4.0: How Does the Paradigm Shift Treat the Role of Human Learning? Procedia Manuf. 2018, 23, 117–122. [Google Scholar] [CrossRef]
  138. Kanawaday, A.; Sane, A. Machine learning for predictive maintenance of industrial machines using IoT sensor data. In Proceedings of the 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 24–26 November 2017; pp. 87–90. [Google Scholar] [CrossRef]
  139. Mohammadi, M.; Al-Fuqaha, A.; Sorour, S.; Guizani, M. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun. Surv. Tutor. 2018, 20, 2923–2960. [Google Scholar] [CrossRef] [Green Version]
  140. Brik, B.; Bettayeb, B.; Sahnoun, M.; Duval, F. Towards Predicting System Disruption in Industry 4.0: Machine Learning-Based Approach. Procedia Comput. Sci. 2019, 151, 667–674. [Google Scholar] [CrossRef]
  141. Fahle, S.; Prinz, C.; Kuhlenkötter, B. Systematic review on machine learning (ML) methods for manufacturing processes—Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 2020, 93, 413–418. [Google Scholar] [CrossRef]
  142. Singer, G.; Cohen, Y. A framework for smart control using machine-learning modeling for processes with closed-loop control in Industry 4.0. Eng. Appl. Artif. Intell. 2021, 102, 104236. [Google Scholar] [CrossRef]
  143. Luna-Perejón, F.; Montes-Sánchez, J.M.; Durán-López, L.; Vazquez-Baeza, A.; Beasley-Bohórquez, I.; Sevillano-Ramos, J.L. IoT Device for Sitting Posture Classification Using Artificial Neural Networks. Electronics 2021, 10, 1825. [Google Scholar] [CrossRef]
  144. Ahmed, K.I.; Tahir, M.; Habaebi, M.H.; Lau, S.L.; Ahad, A. Machine Learning for Authentication and Authorization in IoT: Taxonomy, Challenges and Future Research Direction. Sensors 2021, 21, 5122. [Google Scholar] [CrossRef] [PubMed]
  145. Nagar, D.; Raghav, S.; Bhardwaj, A.; Kumar, R.; Lata Singh, P.; Sindhwani, R. Machine learning: Best way to sustain the supply chain in the era of industry 4.0. Mater. Today Proc. 2021, 47, 3676–3682. [Google Scholar] [CrossRef]
  146. Resende, C.; Folgado, D.; Oliveira, J.; Franco, B.; Moreira, W.; Oliveira, A., Jr.; Cavaleiro, A.; Carvalho, R. TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance. Sensors 2021, 21, 4676. [Google Scholar] [CrossRef]
  147. Calabrese, M.; Cimmino, M.; Fiume, F.; Manfrin, M.; Romeo, L.; Ceccacci, S.; Paolanti, M.; Toscano, G.; Ciandrini, G.; Carrotta, A.; et al. SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0. Information 2020, 11, 202. [Google Scholar] [CrossRef] [Green Version]
  148. Vaccari, I.; Chiola, G.; Aiello, M.; Mongelli, M.; Cambiaso, E. MQTTset, a New Dataset for Machine Learning Techniques on MQTT. Sensors 2020, 20, 6578. [Google Scholar] [CrossRef] [PubMed]
  149. Moens, P.; Bracke, V.; Soete, C.; Vanden Hautte, S.; Avendano, D.N.; Ooijevaar, T.; Devos, S.; Volckaert, B.; Hoecke, S. Van Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications. Sensors 2020, 20, 4308. [Google Scholar] [CrossRef] [PubMed]
  150. Merenda, M.; Porcaro, C.; Iero, D. Edge Machine Learning for AI-Enabled IoT Devices: A Review. Sensors 2020, 20, 2533. [Google Scholar] [CrossRef]
  151. Schumacher, A.; Nemeth, T.; Sihn, W. Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises. Procedia CIRP 2019, 79, 409–414. [Google Scholar] [CrossRef]
  152. Ganzarain, J.; Errasti, N. Three stage maturity model in SME’s towards industry 4.0. J. Ind. Eng. Manag. 2016, 9, 1119–1128. [Google Scholar] [CrossRef]
  153. Canetta, L.; Barni, A.; Montini, E. Development of a Digitalization Maturity Model for the Manufacturing Sector. In Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Stuttgart, Germany, 17–20 June 2018. [Google Scholar] [CrossRef]
  154. Lorenzo Ochoa, O. Modelos de madurez digital: ¿en qué consisten y qué podemos aprender de ellos? Bol. Estud. Económ. 2016, 71, 573–590. [Google Scholar]
  155. Secretaría General de Industria y de la Pequeña y Mediana Empresa HADA—Herramienta de Autodiagnóstico Avanzado para la Evaluación de la Madurez Digital. Manual Usuario. Available online: https://hada.industriaconectada40.gob.es/data/manual/Manual_usuario_HADA.pdf (accessed on 12 February 2021).
  156. Jacquez, M.V.; López, V.G. Modelos de evaluación de la madurez y preparación hacia la Industria 4.0: Una revisión de literatura. Ing. Ind. Actual. Nuevas Tend. 2018, 11, 61–78. [Google Scholar]
  157. Leyh, C.; Bley, K.; Schaffer, T.; Forstenhausler, S. SIMMI 4.0-a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0. Ann. Comput. Sci. Inf. Syst. 2016, 8, 1297–1302. [Google Scholar] [CrossRef]
  158. Schumacher, A.; Erol, S.; Sihn, W. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP 2016, 52, 161–166. [Google Scholar] [CrossRef]
  159. Tonelli, F.; Demartini, M.; Loleo, A.; Testa, C. A Novel Methodology for Manufacturing Firms Value Modeling and Mapping to Improve Operational Performance in the Industry 4.0 Era. Procedia CIRP 2016, 57, 122–127. [Google Scholar] [CrossRef]
  160. Otero Mateo, M.; Cerezo Narvaez, A.; Pastor Fernandez, A.; Rodriguez Pecci, F. Transformación Digital De Requisitos En La Industria 4.0: Caso De Plataformas Navales. Dyna Ing. Ind. 2018, 93, 448–456. [Google Scholar] [CrossRef] [Green Version]
  161. Roldán, J.J.; Crespo, E.; Martín-Barrio, A.; Peña-Tapia, E.; Barrientos, A. A training system for Industry 4.0 operators in complex assemblies based on virtual reality and process mining. Robot. Comput. Integr. Manuf. 2019, 59, 305–316. [Google Scholar] [CrossRef]
  162. Liagkou, V.; Salmas, D.; Stylios, C. Realizing Virtual Reality Learning Environment for Industry 4.0. Procedia CIRP 2019, 79, 712–717. [Google Scholar] [CrossRef]
  163. Damiani, L.; Demartini, M.; Guizzi, G.; Revetria, R.; Tonelli, F. Augmented and virtual reality applications in industrial systems: A qualitative review towards the industry 4.0 era. IFAC-PapersOnLine 2018, 51, 624–630. [Google Scholar] [CrossRef]
  164. Mourtzis, D.; Zogopoulos, V.; Vlachou, E. Augmented Reality Application to Support Remote Maintenance as a Service in the Robotics Industry. Procedia CIRP 2017, 63, 46–51. [Google Scholar] [CrossRef]
  165. Syberfeldt, A.; Danielsson, O.; Gustavsson, P. Augmented Reality Smart Glasses in the Smart Factory: Product Evaluation Guidelines and Review of Available Products. IEEE Access 2017, 5, 9118–9130. [Google Scholar] [CrossRef]
  166. Firu, A.C.; Tapîrdea, A.I.; Feier, A.I.; Draghici, G. Virtual reality in the automotive field in industry 4.0. Mater. Today Proc. 2021, 45, 4177–4182. [Google Scholar] [CrossRef]
  167. Ceruti, A.; Marzocca, P.; Liverani, A.; Bil, C. Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing. J. Comput. Des. Eng. 2019, 6, 516–526. [Google Scholar] [CrossRef]
  168. Wolfartsberger, J.; Zenisek, J.; Sievi, C. Chances and Limitations of a Virtual Reality-supported Tool for Decision Making in Industrial Engineering. IFAC-PapersOnLine 2018, 51, 637–642. [Google Scholar] [CrossRef]
  169. Lamberti, F.; Pescador, F. Advanced Interaction and Virtual\/Augmented Reality-Part II: A Look at Novel Applications. IEEE Consum. Electron. Mag. 2018, 7, 62–63. [Google Scholar] [CrossRef]
  170. Marino, E.; Barbieri, L.; Colacino, B.; Fleri, A.K.; Bruno, F. An Augmented Reality inspection tool to support workers in Industry 4.0 environments. Comput. Ind. 2021, 127, 103412. [Google Scholar] [CrossRef]
  171. Gattullo, M.; Scurati, G.W.; Fiorentino, M.; Uva, A.E.; Ferrise, F.; Bordegoni, M. Towards augmented reality manuals for industry 4.0: A methodology. Robot. Comput. Integr. Manuf. 2019, 56, 276–286. [Google Scholar] [CrossRef]
  172. Paszkiewicz, A.; Salach, M.; Dymora, P.; Bolanowski, M.; Budzik, G.; Kubiak, P. Methodology of Implementing Virtual Reality in Education for Industry 4.0. Sustainability 2021, 13, 5049. [Google Scholar] [CrossRef]
  173. Hu, M.; Luo, X.; Chen, J.; Lee, Y.C.; Zhou, Y.; Wu, D. Virtual reality: A survey of enabling technologies and its applications in IoT. J. Netw. Comput. Appl. 2021, 178, 102970. [Google Scholar] [CrossRef]
  174. Masood, T.; Egger, J. Augmented reality in support of Industry 4.0—Implementation challenges and success factors. Robot. Comput. Integr. Manuf. 2019, 58, 181–195. [Google Scholar] [CrossRef]
  175. Salah, B.; Abidi, M.H.; Mian, S.H.; Krid, M.; Alkhalefah, H.; Abdo, A. Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0. Sustainability 2019, 11, 1477. [Google Scholar] [CrossRef] [Green Version]
  176. Lanyi, C.S.; Withers, J.D.A. Striving for a Safer and More Ergonomic Workplace: Acceptability and Human Factors Related to the Adoption of AR/VR Glasses in Industry 4.0. Smart Cities 2020, 3, 289–307. [Google Scholar] [CrossRef]
  177. Vidal-Balea, A.; Blanco-Novoa, O.; Fraga-Lamas, P.; Vilar-Montesinos, M.; Fernández-Caramés, T.M. Creating Collaborative Augmented Reality Experiences for Industry 4.0 Training and Assistance Applications: Performance Evaluation in the Shipyard of the Future. Appl. Sci. 2020, 10, 9073. [Google Scholar] [CrossRef]
  178. Al-Jaroodi, J.; Mohamed, N. Blockchain in Industries: A Survey. IEEE Access 2019, 7, 36500–36515. [Google Scholar] [CrossRef]
  179. Alladi, T.; Chamola, V.; Parizi, R.M.; Choo, K.K.R. Blockchain Applications for Industry 4.0 and Industrial IoT: A Review. IEEE Access 2019, 7, 176935–176951. [Google Scholar] [CrossRef]
  180. Aoun, A.; Ilinca, A.; Ghandour, M.; Ibrahim, H. A review of Industry 4.0 characteristics and challenges, with potential improvements using Blockchain technology. Comput. Ind. Eng. 2021, 162, 107746. [Google Scholar] [CrossRef]
  181. Bellavista, P.; Esposito, C.; Foschini, L.; Giannelli, C.; Mazzocca, N.; Montanari, R. Interoperable Blockchains for Highly-Integrated Supply Chains in Collaborative Manufacturing. Sensors 2021, 21, 4955. [Google Scholar] [CrossRef]
  182. Bodkhe, U.; Tanwar, S.; Parekh, K.; Khanpara, P.; Tyagi, S.; Kumar, N.; Alazab, M. Blockchain for Industry 4.0: A comprehensive review. IEEE Access 2020, 8, 79764–79800. [Google Scholar] [CrossRef]
  183. ElMamy, S.B.; Mrabet, H.; Gharbi, H.; Jemai, A.; Trentesaux, D. A Survey on the Usage of Blockchain Technology for Cyber-Threats in the Context of Industry 4.0. Sustainability 2020, 12, 9179. [Google Scholar] [CrossRef]
  184. Ferreira, C.M.S.; Oliveira, R.A.R.; Silva, J.S.; da Cunha Cavalcanti, C.F.M. Blockchain for Machine to Machine Interaction in Industry 4.0. In Blockchain Technology for Industry; Springer: Singapore, 2020; pp. 99–116. [Google Scholar] [CrossRef]
  185. Hennebert, C.; Barrois, F. Is the blockchain a relevant technology for the industry 4.0? In Proceedings of the 2020 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France, 28–30 September 2020; pp. 212–216. [Google Scholar] [CrossRef]
  186. Jang, S.H.; Guejong, J.; Jeong, J.; Sangmin, B. Fog Computing Architecture Based Blockchain for Industrial IoT. In International Conference on Computational Science; Springer: Cham, Switzerland, 2019; pp. 593–606. [Google Scholar] [CrossRef]
  187. Javaid, M.; Haleem, A.; Pratap Singh, R.; Khan, S.; Suman, R. Blockchain technology applications for Industry 4.0: A literature-based review. Blockchain Res. Appl. 2021, 100027. [Google Scholar] [CrossRef]
  188. Kapitonov, A.; Berman, I.; Lonshakov, S.; Krupenkin, A. Blockchain based protocol for economical communication in industry 4.0. In Proceedings of the 2018 Crypto Valley Conference on Blockchain Technology, CVCBT 2018, Zug, Switzerland, 20–22 June 2018; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2018; pp. 41–44. [Google Scholar]
  189. Khanfar, A.A.A.; Iranmanesh, M.; Ghobakhloo, M.; Senali, M.G.; Fathi, M. Applications of Blockchain Technology in Sustainable Manufacturing and Supply Chain Management: A Systematic Review. Sustainability 2021, 13, 7870. [Google Scholar] [CrossRef]
  190. Leng, J.; Ye, S.; Zhou, M.; Zhao, J.L.; Liu, Q.; Guo, W.; Cao, W.; Fu, L. Blockchain-Secured Smart Manufacturing in Industry 4.0: A Survey. IEEE Trans. Syst. Man Cybern. Syst. 2021, 51, 237–252. [Google Scholar] [CrossRef]
  191. Mushtaq, A.; Haq, I.U. Implications of blockchain in industry 4.O. In Proceedings of the 2019 International Conference on Engineering and Emerging Technologies (ICEET), Lahore, Pakistan, 21–22 February 2019. [Google Scholar] [CrossRef]
  192. Rathee, G.; Balasaraswathi, M.; Chandran, K.P.; Gupta, S.D.; Boopathi, C.S. A secure IoT sensors communication in industry 4.0 using blockchain technology. J. Ambient Intell. Humaniz. Comput. 2021, 12, 533–545. [Google Scholar] [CrossRef]
  193. Sabri-Laghaie, K.; Ghoushchi, S.J.; Elhambakhsh, F.; Mardani, A. Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0). Algorithms 2020, 13, 312. [Google Scholar] [CrossRef]
  194. Singh, M. Blockchain Technology for Data Management in Industry 4.0; Springer: Berlin, Germany, 2020; pp. 59–72. [Google Scholar] [CrossRef]
  195. Umran, S.M.; Lu, S.; Abduljabbar, Z.A.; Zhu, J.; Wu, J. Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology. Appl. Sci. 2021, 11, 6376. [Google Scholar] [CrossRef]
  196. Swami, M.; Verma, D.; Vishwakarma, V.P. Blockchain and Industrial Internet of Things: Applications for Industry 4.0. Adv. Intell. Syst. Comput. 2021, 1164, 279–290. [Google Scholar] [CrossRef]
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Figure 1. The main steps taken to validate the research.
Figure 1. The main steps taken to validate the research.
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Figure 2. (a) Ratio of exclusion and inclusion of documents of each paradigm and key technology; (b) Percentage of documents included and excluded with respect to the total.
Figure 2. (a) Ratio of exclusion and inclusion of documents of each paradigm and key technology; (b) Percentage of documents included and excluded with respect to the total.
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Figure 3. Geographical distribution and density of publications.
Figure 3. Geographical distribution and density of publications.
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Figure 4. Percentage of countries’ contribution.
Figure 4. Percentage of countries’ contribution.
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Figure 5. Chronology of scientific information on each technology and paradigm.
Figure 5. Chronology of scientific information on each technology and paradigm.
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Figure 6. (a) Percentage ratio of scientific production by years; (b) distribution of scientific information by year.
Figure 6. (a) Percentage ratio of scientific production by years; (b) distribution of scientific information by year.
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Figure 7. Keywords most frequently used.
Figure 7. Keywords most frequently used.
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Figure 8. Leading journals in this work.
Figure 8. Leading journals in this work.
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Figure 9. Language ratio.
Figure 9. Language ratio.
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Figure 10. Proposed scheme of reorganization of Industry 4.0 criteria, paradigms, and key technologies.
Figure 10. Proposed scheme of reorganization of Industry 4.0 criteria, paradigms, and key technologies.
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Figure 11. Proposed CSCW matrix for Industry 4.0 paradigms and key technologies.
Figure 11. Proposed CSCW matrix for Industry 4.0 paradigms and key technologies.
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Table
Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
CategoryCriteriaCode
InclusionThe article corresponds to a scientific database and formally published.Y01
Articles completely written in English or SpanishY02
The article addresses the topic of Industry 4.0 in a major way.Y03
The article mentions key paradigms and technologies involved in Industry 4.0 concept.Y04
The article relates the key technology to Industry 4.0.Y05
The article is updated no more than 5 years in the case of key technologies.Y06
The article details the contribution of the technology in question to Industry 4.0.Y07
If it is a web page, it must correspond to official scientific dissemination sites.Y08
ExclusionThe article is not published in scientific databases and/or indexed journals.N01
The quality of the information is irrelevant for this study.N02
The information is redundant and of lower quality than previously included articles.N03
The article deals with emerging technologies but does not relate them to Industry 4.0.N04
The article deals with very particular case studies and with little detail.N05
The article mentions Industry 4.0 in its title or key words but does not address the topic in its content.N06
The information in the article is outdated with respect to others previously included.N07
If it is a web page, it corresponds to blogs or unofficial web pages.N08
Table
Table 2. Technologies and paradigms of Industry 4.0 analyzed.
Table 2. Technologies and paradigms of Industry 4.0 analyzed.
Technology/ParadigmIdentified DocumentsExcluded DocumentsIncluded DocumentsReferences
Industry 4.0554015[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
Cyber–Physical Systems61556[16,17,18,19,20,21]
IoT–IIoT12010317[22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]
Cyber security443014[39,40,41,42,43,44,45,46,47,48,49,50,51,52]
Big Data and Analytics82739[53,54,55,56,57,58,59,60,61]
Big Data in Industry 4.0685513[62,63,64,65,66,67,68,69,70,71,72,73,74]
Digital Twin452916[75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90]
Cloud, Fog, Edge computing26524025[91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115]
5G in Industry 4.0786414[116,117,118,119,120,121,122,123,124,125,126,127,128,129]
AI in Industry 4.019817721[130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150]
Digital Maturity of Industry463610[151,152,153,154,155,156,157,158,159,160]
Virtual/Augmented Reality725517[161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177]
Blockchain in Industry 4.0523220[178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196]
Total1186989197
Table
Table 3. Scientific contribution by country.
Table 3. Scientific contribution by country.
CountryNumber of Documents
U.S.A.28
China27
Spain22
Italy18
Germany16
India12
Australia10
United Kingdom10
Canada, South Korea8
France7
Brazil7
Austria5
Colombia, Sweden, Turkey, Portugal, Greece4
Ireland, Poland, Romania, Finland, Saudi Arabia,
Malaysia, Denmark		3
Switzerland, Lithuania, Taiwan, Singapore, Pakistan, Iran, Belgium, New Zealand, Hungary2
Slovakia, Ecuador, Russia, Norway, México, Morocco, Egypt, Estonia, Argentina, Macedonia, Malta,
Czech Republic, the Netherlands, Jordan, Seoul, Israel, Mexico, Palestine, Lebanon, Tunisia, Iraq		1
Table
Table 4. Technologies and paradigms publication year.
Table 4. Technologies and paradigms publication year.
Technology/Paradigm200820092012201320142015201620172018201920202021
Industry 4.0 143313
Cyber–Physical Systems 1 122
IoT–IIoT1 2113 27
Cyber security 24116
Big Data and Analytics 12 111 3
Big Data and Industry 4.0 111 2212 3
Digital Twin 12 112
Cloud computing, Fog computing, Edge computing 1 1 4 514
5G and Industry 4.0 1 11 326
AI and Industry 4.0 4197
Digital Maturity of Industry 5131
Virtual/Augmented Reality 23624
Blockchain in Industry 4.0 1468
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Rocha-Jácome, C.; Carvajal, R.G.; Chavero, F.M.; Guevara-Cabezas, E.; Hidalgo Fort, E. Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review. Sensors 2022, 22, 66. https://doi.org/10.3390/s22010066

AMA Style

Rocha-Jácome C, Carvajal RG, Chavero FM, Guevara-Cabezas E, Hidalgo Fort E. Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review. Sensors. 2022; 22(1):66. https://doi.org/10.3390/s22010066

Chicago/Turabian Style

Rocha-Jácome, Cristian, Ramón González Carvajal, Fernando Muñoz Chavero, Esteban Guevara-Cabezas, and Eduardo Hidalgo Fort. 2022. "Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review" Sensors 22, no. 1: 66. https://doi.org/10.3390/s22010066

APA Style

Rocha-Jácome, C., Carvajal, R. G., Chavero, F. M., Guevara-Cabezas, E., & Hidalgo Fort, E. (2022). Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review. Sensors, 22(1), 66. https://doi.org/10.3390/s22010066

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