Abstract
Recent advances in the large-scale adoption of information and communication technologies in manufacturing processes, known as Industry 4.0 or Smart Manufacturing, provide us a window into how the manufacturing sector will evolve in the coming decades. As a result of these initiatives, manufacturing firms have started to integrate a series of emerging technologies into their processes that will change the way products are designed, manufactured, and consumed. This article provides a comprehensive review of how service-oriented computing is being employed to develop the required software infrastructure for Industry 4.0 and identifies the major challenges and research opportunities that ensue. Particular attention is paid to the microservices architecture, which is increasingly recognized as offering a promising approach for developing innovative industrial applications. This literature review is based on the current state of the art on service computing for Industry 4.0 as described in a large corpus of recently published research papers, which helped us to identify and explore a series of challenges and opportunities for the development of this emerging technology frontier, with the goal of facilitating its widespread adoption.
- M. Aazam, S. Zeadally, and K. A. Harras. 2018. Deploying fog computing in industrial Internet of Things and industry 4.0. IEEE Trans. Industr. Inf. 14, 10 (2018), 4674–4682.Google ScholarCross Ref
- B. M. Agostinho, C. B. de Souza, F. O. Gomes, A. S. R. Pinto, and M. A. R. Dantas. 2020. Omniconn: An architecture for heterogeneous devices interoperability on industrial Internet of Things. In Lecture Notes in Networks and Systems. Vol. 96. 329–339.Google Scholar
- E. Al-Masri. 2018. Enhancing the microservices architecture for the Internet of Things. In Proceedings of the 2018 IEEE International Conference on Big Data. 5119–5125.Google ScholarCross Ref
- A. B. A. Alaasam, G. Radchenko, and A. Tchernykh. 2019. Stateful stream processing for digital twins: Microservice-based kafka stream DSL. In Proceedings of the International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON’19). 804–809.Google Scholar
- K. Alexopoulos, K. Sipsas, E. Xanthakis, S. Makris, and D. Mourtzis. 2018. An industrial Internet of things based platform for context-aware information services in manufacturing. Intl. J. Comput. Integr. Manufact. 31, 11 (2018), 1111–1123.Google ScholarCross Ref
- A. Angelopoulos, E. T. Michailidis, N. Nomikos, P. Trakadas, A. Hatziefremidis, S. Voliotis, and T. Zahariadis. 2020. Tackling faults in the industry 4.0 era—A survey of machine-learning solutions and key aspects. Sensors 20, 1 (2020), 109.Google ScholarCross Ref
- P. O. Antonino, F. Schnicke, Z. Zhang, and T. Kuhn. 2019. Blueprints for architecture drivers and architecture solutions for Industry 4.0 shopfloor applications. In Proceedings of the 13th European Conference on Software Architecture (ECSA’19). 261–268.Google Scholar
- U. D. Atmojo, J. O. Blech, S. Sierla, and V. Vyatkin. 2019. Service-based architecture with product-centric control in a production island-based agile factory. In Proceedings of the IEEE International Conference on Industrial Internet (ICII’19). 305–306.Google Scholar
- R. Badarinath and V.V. Prabhu. 2017. Advances in internet of things (IoT) in manufacturing. IFIP Adv. Inf. Commun. Technol. 513 (2017), 111–118.Google ScholarCross Ref
- S. Bader, E. Barnstedt, H. Bedenbender, M. Billman, B. Boss, and A. Braunmandl. 2020. Details of the Asset Administration Shell. Retrieved from https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/Details_of_the_Asset_Administration_Shell_Part1_V3.pdf.Google Scholar
- A. Bagozi and D. Bianchini. 2019. IDEAaS: Interactive data exploration as-a service. In Proceedings of the 2019 IEEE World Congress on Services. 345–348.Google Scholar
- A. Bagozi, D. Bianchini, V. D. Antonellis, A. Marini, and D. Ragazzi. 2017. Interactive data exploration as a service for the smart factory. In Proceedings of the 2017 IEEE 24th International Conference on Web Services (ICWS’17). 293–300.Google Scholar
- H. Baumgartel and R. Verbeet. 2020. Service and agent based system architectures for industrie 4.0 systems. In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS’20).Google Scholar
- T. Berners-Lee, J. Hendler, and O. Lassila. 2001. The semantic web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Sci. Am. 284, 5 (2001), 34–43.Google Scholar
- J. A. Bigheti, M. M. Fernandes, and E. P. Godoy. 2019. Control as a service: A microservice approach to industry 4.0. In Proceedings of the IEEE International Workshop on Metrology for Industry 4.0 and IoT (MetroInd 4.0 and IoT’19). 438–443.Google Scholar
- H. P. Breivold. 2017. Internet-of-things and cloud computing for smart industry: A systematic mapping study. In Proceedings of the 5th International Conference on Enterprise Systems. 299–304.Google ScholarCross Ref
- J. Buenabad-Chávez, G. Kecskemeti, V. Tountopoulos, E. Kavakli, and R. Sakellariou. 2018. Towards a methodology for RAMI4.0 service design. In Proceedings of the 6th International Conference on Enterprise Systems (ES’18). 188–195.Google Scholar
- F. Burzlaff and C. Bartelt. 2018. I4.0-device integration: A qualitative analysis of methods and technologies utilized by system integrators: Implications for enginering future industrial Internet of Things system. In Proceedings of the IEEE 15th International Conference on Software Architecture Companion, (ICSA-C’18). 27–34.Google Scholar
- A. Buzachis, A. Galletta, A. Celesti, L. Carnevale, and M. Villari. 2019. Towards osmotic computing: A blue-green strategy for the fast re-deployment of microservices. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC’19).Google Scholar
- R. L. Cagnin, I. R. Guilherme, J. Queiroz, B. Paulo, and M. F. O. Neto. 2018. A multi-agent system approach for management of industrial iot devices in manufacturing processes. In Proceedings of the IEEE 16th International Conference on Industrial Informatics (INDIN’18). 31–36.Google ScholarCross Ref
- M. Ciavotta, M. Alge, S. Menato, D. Rovere, and P. Pedrazzoli. 2017. A microservice-based middleware for the digital factory. Proc. Manufact. 11(Jun.2017), 931–938.Google Scholar
- K. Dang and I. Trotskii. 2019. Architecture for automation system metrics collection, visualization and data engineering—HAMK sheet metal center building automation case study. Open Eng. 9, 1 (2019), 561–570.Google ScholarCross Ref
- A. V. Dastjerdi and R. Buyya. 2016. Fog computing: Helping the Internet of Things realize its potential. Computer 49, 8 (2016), 112–116.Google ScholarDigital Library
- L. B. R. de Oliveira, K. Romero Felizardo, D. Feitosa, and E. Y. Nakagawa. 2010. Reference models and reference architectures based on service-oriented architecture: A systematic review. In Software Architecture. 360–367.Google Scholar
- H. Derhamy, J. Eliasson, and J. Delsing. 2017. IoT interoperability - on-demand and low latency transparent multiprotocol translator. IEEE IoT J. 4, 5 (2017), 1754–1763.Google Scholar
- H. Derhamy, J. Eliasson, and J. Delsing. 2019. System of system composition based on decentralized service-oriented architecture. IEEE Syst. J. 13, 4 (2019), 3675–3686.Google ScholarCross Ref
- H. Derhamy, J. Ronnholm, J. Delsing, J. Eliasson, and J. Van Deventer. 2017. Protocol interoperability of OPC UA in service oriented architectures. In Proceedings of the IEEE 15th International Conference on Industrial Informatics (INDIN’17). 44–50.Google Scholar
- P. Di Francesco, I. Malavolta, and P. Lago. 2017. Research on architecting microservices: Trends, focus, and potential for industrial adoption. In IEEE International Conference on Software Architecture (ICSA’17). 21–30.Google Scholar
- G. di Orio, P. Malo, and J. Barata. 2019. NOVAAS: A reference implementation of industrie4.0 asset administration shell with best-of-breed practices from IT engineering. In Proceedings of the 45th Annual Conference of the IEEE Industrial Electronics Society (IECON’19). 5505–5512.Google Scholar
- J. Dobaj, M. Krisper, J. Iber, and C. Kreiner. 2018. A microservice architecture for the industrial internet-of-things. In Proceedings of the 23rd European Conference on Pattern Languages of Programs (EuroPLoP’18). 1–15.Google Scholar
- J. Dobaj, M. Krisper, and G. Macher. 2019. Towards cyber-physical infrastructure as-a-service (CPIaaS) in the era of industry 4.0. In Proceedings of the European Conference on Software Process Improvement. 310–321.Google Scholar
- S. Dobrescu, O. Chenaru, N. Matei, L. Ichim, and D. Popescu. 2016. A service oriented system of reusable algorithms for distributed control of petroleum facilities in onshore oilfields. In Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI’16). 1–6.Google Scholar
- J. Eliasson, J. Delsing, H. Derhamy, Z. Salcic, and K. Wang. 2015. Towards industrial Internet of Things: An efficient and interoperable communication framework. In Proceedings of the IEEE International Conference on Industrial Technology. 2198–2204.Google Scholar
- M. Engelsberger and T. Greiner. 2018. Dynamic reconfiguration of service-oriented resources in cyber–physical production systems by a process-independent approach with multiple criteria and multiple resource management operations. Fut. Gen. Comput. Syst. 88 (2018), 424–441.Google ScholarCross Ref
- S. M. Fallah. 2015. Multi agent based control architectures. In Proceedings of the 26th DAAAM International Symposium on Intelligent Manifacturing and Automation. 1166–1170.Google Scholar
- F. Ferreira, J. Faria, A. Azevedo, and A. L. Marques. 2016. Product lifecycle management enabled by industry 4.0 technology. Adv. Transdiscipl. Eng. 3 (2016), 349–354.Google Scholar
- B. R. Ferrer and J. L. M. Lastra. 2017. An architecture for implementing private local automation clouds built by CPS. In Proceedings of the 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON’17), 5406–5413.Google ScholarCross Ref
- R. T. Fielding. 2000. REST: Architectural Styles and the Design of Network-based Software Architectures. Ph.D. Dissertation. University of California, Irvine.Google Scholar
- S. Figueroa-Lorenzo, J. Añorga, and S. Arrizabalaga. 2020. A survey of IIoT protocols: A measure of vulnerability risk analysis based on CVSS. ACM Comput. Surv. 53, 2, Article 44 (Apr. 2020), 53 pages.Google Scholar
- J. C. Garcia-Ortiz, D. Todoli-Ferrandis, J. Vera-Perez, S. Santonja-Climent, and V. Sempere-Paya. 2019. Design of a micro-service based Data Pool for device integration to speed up digitalization. In Proceedings of the 27th Telecommunications Forum (TELFOR’19).Google Scholar
- R. Geissbauer, J. Vedso, and S. Schrauf. 2016. Industry 4.0: Building the Digital Enterprise. Retrieved from https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016.pdf.Google Scholar
- A. Gilchrist. 2016. Industry 4.0: the Industrial Internet of Things. Apress, New York.Google Scholar
- T. Glock, V. Pazmino Betancourt, M. Kern, B. Liu, T. Reib, E. Sax, and J. Becker. 2019. Service-based industry 4.0 middleware for partly automated collaborative work of cranes. In Proceedings of the of 8th International Conference on Industrial Technology and Management, ICITM 2019. 229–235.Google Scholar
- I. Grangel-Gonzalez. 2018. A Knowledge Graph Based Integration Approach for Industry 4.0. Ph.D. Dissertation. University of Bonn.Google Scholar
- I. Grangel-Gonzalez, P. Baptista, L. Halilaj, S. Lohmann, M. E. Vidal, C. Mader, and S. Auer. 2017. The industry 4.0 standards landscape from a semantic integration perspective. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA’17). 1–8.Google Scholar
- M. Grieves. 2014. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. Retrieved from https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication. White Paper.Google Scholar
- M. Grieves and J. Vickers. 2016. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, F. J. Kahlen, S. Flumerfelt, and A. Alves (Eds.). 1–327.Google Scholar
- C. Gröger, L. Kassner, E. Hoos, J. Königsberger, C. Kiefer, S. Silcher, and B. Mitschang. 2016. The data-driven factory: Leveraging big industrial data for agile, learning and human-centric manufacturing. In Proceedings of the of the 18th International Conference on Enterprise Information Systems. 40–52.Google Scholar
- S. Grüner, J. Pfrommer, and F. Palm. 2016. RESTful industrial comunication with OPC UA. IEEE Trans. Industr. Inf. 12, 5 (2016), 1832–1841.Google ScholarCross Ref
- V. Hugud and S. K. Arunachalam. 2020. Digital twin: Empowering edge devices to be intelligent. In The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases. 107–126.Google Scholar
- S. Iarovyi, J. L. M. Lastra, R. Haber, and R. Del Toro. 2015. From artificial cognitive systems and open architectures to cognitive manufacturing systems. In Proceedings of the IEEE International Conference on Industrial Informatics (INDIN’15). 1225–1232.Google Scholar
- J. Innerbichler, S. Gonul, V. Damjanovic-Behrendt, B. Mandler, and F. Strohmeier. 2017. NIMBLE collaborative platform: Microservice architectural approach to federated IoT. In Proceedings of the Global Internet of Things Summit (GIoTS’17).Google Scholar
- H. Ishiguro, F. Yamaoka, T. Kanda, B. Mutlu, and N. Hagita. 2017. The Industrial Internet of Things Volume G1: Reference Architecture. Retrieved from https://www.iiconsortium.org/IIC_PUB_G1_V1.80_2017-01-31.pdf.Google Scholar
- A. Ismail and W. Kastner. 2016. A middleware architecture for vertical integration. In Proceedings of the 1st International Workshop on Cyber-Physical Production Systems (CPPS’16). 1–4.Google Scholar
- A. Ismail and W. Kastner. 2016. Discovery in SOA-governed industrial middleware with mDNS and DNS-SD. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA’16).Google Scholar
- P. Jamshidi, C. Pahl, N. C. Mendonça, J. Lewis, and S. Tilkov. 2018. Microservices: The journey so far and challenges ahead. IEEE Softw. 35, 3 (2018), 24–35. DOI: https://doi.org/10.1109/MS.2018.2141039Google ScholarCross Ref
- V. Jirkovský, M. Obitko, and V. Mařík. 2017. Understanding data heterogeneity in the context of cyber-physical systems integration. IEEE Trans. Industr. Inf. 13, 2 (2017), 660–667.Google ScholarCross Ref
- K. Kayabay, M. O. Gokalp, P. E. Eren, and A. Kocyigit. 2019. A workflow and cloud based service-oriented architecture for distributed manufacturing in industry 4.0 context. In Proceedings of the IEEE 11th International Conference on Service-Oriented Computing and Applications (SOCA’18). 88–92.Google Scholar
- E. Kharlamov, D. Hovland, M. G. Skjæveland, D. Bilidas, E. Jiménez-Ruiz, G. Xiao, A. Soylu, D. Lanti, M. Rezk, D. Zheleznyakov, M. Giese, H. Lie, Y. Ioannidis, Y. Kotidis, M. Koubarakis, and A. Waaler. 2017. Ontology based data access in statoil. J. Web Semant. 44 (2017), 3–36.Google ScholarDigital Library
- M. S. Knudsen, J. Kaivo-Oja, and T. Lauraeus. 2019. Enabling technologies of industry 4.0 and their global forerunners: An empirical study of the web of science database. In Communications in Computer and Information Science. Vol. 1027. 3–13.Google Scholar
- J. Kolb, M. AbdelBaky, R. H. Katz, and D. E. Culler. 2020. Core concepts, challenges, and future directions in blockchain: A centralized tutorial. ACM Comput. Surv. 53, 1, Article 9 (Feb. 2020), 39 pages.Google Scholar
- P. Kolyvakis, M.-J. Yoo, and D. Kiritsis. 2017. Knowledge as a service in the IoT era. In Proceedings of the 1st International Conference on Internet of Things and Machine Learning (IML’17). 1–6.Google Scholar
- T. Kuhn, S. Sadikow, and P. Antonino. 2019. A service-based production ecosystem architecture for industrie 4.0. Kunstl. Intell. 33, 2 (2019), 163–169.Google ScholarCross Ref
- B. (Serm) Kulvatunyou, H. Oh, N. Ivezic, and S. T. Nieman. 2019. Standards-based semantic integration of manufacturing information: Past, present, and future. J. Manufact. Syst. 52, (Jul.2019), 184–197.Google ScholarCross Ref
- P. Lalanda, D. Morand, and P.C. Mora. 2019. Data management in an industrial service-oriented platform. In Proceedings of the IEEE International Conference on Industrial Cyber Physical Systems (ICPS’19). 81–87.Google Scholar
- A. N. Lam and O. Haugen. 2019. Implementing OPC-UA services for industrial cyber-physical systems in service-oriented architecture. In Proceedings of the 45th Annual Conference of the IEEE Industrial Electronics Society (IECON’19). 5486–5492.Google Scholar
- F. Lemoine, T. Aubonnet, and N. Simoni. 2020. Self-assemble-featured Internet of Things. Fut. Gener. Comput. Syst. 112 (2020), 41–57.Google ScholarCross Ref
- J. Lewis and M. Fowler. 2014. Microservices: A definition of this new architectural term. Retrieved from http://martinfowler.com/articles/microservices.html.Google Scholar
- F. Li and L. Gelbke. 2018. Microservice architecture in industrial sofware delivery on edge devices. In Proceedings of the 19th International Conference on Agile Software Development: Companion (XP’18). 1–4.Google Scholar
- B. Liu, T. Glock, V. P. Betancourt, M. Kern, E. Sax, and J. Becker. 2020. Model driven development process for a service-oriented industry 4.0 system. In Proceedings of the 9th International Conference on Industrial Technology and Management (ICITM’20). 78–83.Google Scholar
- J. D. Llamuca, C. A. Garcia, J. E. Naranjo, C. Rosero, E. Alvarez-M, and M. V. Garcia. 2020. Integrating ISA-95 and IEC-61499 for distributed control system monitoring. In Advances in Intelligent Systems and Computing. Vol. 1099. 66–80.Google Scholar
- J. Ma, Q. Wang, and Z. Zhao. 2017. SLAE–CPS: Smart lean automation engine enabled by cyber-physical systems technologies. Sensors (Switz.) 17, 7 (2017), 1–22.Google Scholar
- S. Malakuti, J. Schmitt, M. Platenius-Mohr, S. Grüner, R. Gitzel, and P. Bihani. 2019. A four-layer architecture pattern for constructing and managing digital twins. In Proceedings of the 13th European Conference on Software Architecture (ECSA’19), Vol. 11681. 231–246.Google Scholar
- D. W. McKee, S. J. Clement, J. Almutairi, and J. Xu. 2017. Massive-scale automation in cyber-physical systems: Vision & challenges. In Proceedings of the IEEE 13th International Symposium on Autonomous Decentralized Systems (ISADS’17). 5–11.Google Scholar
- A. Mehdi, E. Kharlamov, D. Stepanova, F. Lösch, and I. Grangel-González. 2019. Towards semantic integration of Bosch manufacturing data. In Proceedings of the ISWC 2019 Satellite Tracks, Vol. 2456. 303–304.Google Scholar
- M. Mena, J. Criado, L. Iribarne, and A. Corral. 2019. Digital dices: Towards the integration of cyber-physical systems merging the web of things and microservices. In Proceedings of the International Conference on Model & Data Engineering (MEDI’19), Lecture Notes in Computer Science, Vol. 11815. 195–205.Google Scholar
- M. Moghaddam, C. R. Kenley, J. M. Colby, M. N. C. Berns, R. Rausch, J. M. W. M. Skeffington, J. Garrity, A. R. Chaturvedi, and A. V. Deshmukh. 2017. Next-generation enterprise architectures. In Proceedings of the IEEE 15th International Conference on Industrial Informatics (INDIN’17). 32–37.Google Scholar
- N. Mohamed, J. Al-Jaroodi, and S. Lazarova-Molnar. 2019. Leveraging the capabilities of industry 4.0 for improving energy efficiency in smart factories. IEEE Access 7 (2019), 18008–18020.Google ScholarCross Ref
- D. Monteiro, R. Gadelha, P. H. M. Maia, L. S. Rocha, and N. C. Mendonça. 2018. Beethoven: An event-driven lightweight platform for microservice orchestration. In Software Architecture, C. E. Cuesta, D. Garlan, and J. Pérez (Eds.). Springer International Publishing, 191–199.Google Scholar
- M. Muller, E. Wings, and L. Bergmann. 2017. Developing open source cyber-physical systems for service-oriented architectures using OPC UA. In Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics (INDIN’17). 83–88.Google Scholar
- M. J. Neuer, F. Marchiori, A. Ebel, N. Matskanis, L. Piedimonti, A. Wolff, and G. Mathis. 2016. Dynamic reallocation and rescheduling of steel products using agents with strategical anticipation and virtual marketstructures. In Proceedings of the 17th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing (MMM’16), Vol. 49. 232–237.Google Scholar
- S. Newman. 2015. Building Microservices: Designing Fine-Grained Systems (1st ed.). O’Reilly Media.Google Scholar
- N. Niknejad, W. Ismail, I. Ghani, B. Nazari, M. Bahari, and A. R. B. C. Hussin. 2020. Understanding service-oriented architecture (SOA): A systematic literature review and directions for further investigation. Inf. Syst. 91 (2020), 101–491.Google ScholarCross Ref
- R. Oberhauser and S. Stigler. 2018. Microflows: Leveraging process mining and an automated constraint recommender for microflow modeling. In Business Modeling and Software Design, B. Shishkov (Ed.). Springer International Publishing, 25–48.Google Scholar
- P. O’Donovan, C. Gallagher, K. Leahy, and D. T. J. O’Sullivan. 2019. A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications. Comput. Industr. 110 (2019), 12–35.Google ScholarCross Ref
- E. Oyekanlu. 2018. Distributed osmotic computing approach to implementation of explainable predictive deep learning at industrial iot network edges with real-time adaptive wavelet graphs. In Proceedings of the 2018 IEEE 1st International Conference on Artificial Intelligence and Knowledge Engineering (AIKE’18). 179–188.Google ScholarCross Ref
- E. Oyekanlu. 2018. Osmotic collaborative computing for machine learning and cybersecurity applications in industrial iot networks and cyber physical systems with Gaussian mixture models. In Proceedings of the 4th IEEE International Conference on Collaboration and Internet Computing (CIC’18). 326–335.Google ScholarCross Ref
- P. Pahlevannejad, A. Herget, R. Moreno, A. Hennecke, and M. Ruskowski. 2019. Implementation and testing of a modular system architecture for generic hybrid production cells in an industrial environment. In Proceedings of the 45th Annual Conference of the IEEE Industrial Electronics Society (IECON’19).Google Scholar
- C. Paniagua, J. Eliasson, and J. Delsing. 2019. Interoperability mismatch challenges in heterogeneous SOA-based systems. In Proceedings of the IEEE International Conference on Industrial Technology, Vol. 2019, 788–793.Google Scholar
- M. P. Papazoglou and A. S. Andreou. 2019. Smart connected digital factories: Unleashing the power of industry 4.0. In Proceedings of the International Conference on Cloud Computing and Services Science (CLOSER’18).Communications in Computer and Information Science, Vol. 1073. 77–101.Google Scholar
- M. P. Papazoglou and W.-J. van den Heuvel. 2007. Service oriented architectures: Approaches, technologies and research issues. VLDB J. 16, 3 (2007), 389–415.Google ScholarDigital Library
- H. S. Park and R. A. Febriani. 2019. Modelling a platform for smart manufacturing system. Proc. Manufact. 38, 2019 (2019), 1660–1667.Google ScholarCross Ref
- K. T. Park, S. J. Im, Y.-S. Kang, S. D. Noh, Y. T. Kang, and S. G. Yang. 2019. Service-oriented platform for smart operation of dyeing and finishing industry. Int. J. Comput. Integr. Manufact. 32, 3 (2019), 307–326.Google ScholarCross Ref
- K. T. Park, D. Lee, and S. D. Noh. 2020. Operation procedures of a work-center-level digital twin for sustainable and smart manufacturing. Int. J. Precis. Eng. Manufact. Green Technol. 7, 3 (2020), 791–814.Google ScholarCross Ref
- K. T. Park, J. Yang, and S. D. Noh. 2021. VREDI: Virtual representation for a digital twin application in a work-center-level asset administration shell. J. Intell. Manufact. 32 (2021), 501–544.Google ScholarCross Ref
- G. Pedone and I. Mezgár. 2018. Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Comput. Industr. 100, (Feb.2018), 278–286.Google Scholar
- R. P. Pontarolli, J. A. Bigheti, M. M. Fernandes, F. O. Domingues, S. L. Risso, and E. P. Godoy. 2020. Microservice orchestration for process control in industry 4.0. In Proceedings of the IEEE International Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT’20). 245–249.Google Scholar
- G. Radchenko, A. Alaasam, and A. Tchernykh. 2019. Micro-workflows: Kafka and kepler fusion to support digital twins of industrial processes. In Proceedings of the 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion’18). 83–88.Google Scholar
- J. Z. Reis and R. F. Gonçalves. 2018. The role of internet of services (IoS) on industry 4.0 through the service oriented architecture (SOA). In IFIP Advances in Information and Communication Technology. Vol. 536. 20–26.Google Scholar
- J. Rufino, M. Alam, J. Ferreira, A. Rehman, and K. F. Tsang. 2017. Orchestration of containerized microservices for IIoT using Docker. In Proceedings of the IEEE International Conference on Industrial Technology. 1532–1536.Google Scholar
- L. Rychener, F. Montet, and J. Hennebert. 2020. Architecture proposal for machine learning based industrial process monitoring. Proc. Comput. Sci. 170, 2019 (2020), 648–655.Google ScholarCross Ref
- T. Sakakura. 2015. A speculation on a framework that provides highly organized services for manufacturing. In Proceedings of the IEEE International Conference on Automation Science and Engineering. 1025–1028.Google ScholarCross Ref
- C. Salkin, M. Oner, A. Ustundag, and E. Cevikcan. 2018. A conceptual framework for industry 4.0. In Industry 4.0: Managing The Digital Transformation, A. Ustundag and E. Cevikcan (Eds.). Springer International, 3–23.Google Scholar
- N. Santos, H. Rodrigues, J. Pereira, F. Morais, R. Abreu, N. Fernandes, D. Martins, and R. J. Machado. 2018. UH4SP - A software platform for integrated management of connected smart plants. In Proceedings of the International Conference on Intelligent Systems (IS’18). 541–548.Google Scholar
- M. Saqlain, M. Piao, Y. Shim, and J. Y. Lee. 2019. Framework of an IoT-based industrial data management for smart manufacturing. J. Sens. Actuator Netw. 8, 25 (2019), 1–21.Google ScholarCross Ref
- M. Satyanarayanan. 2017. The emergence of edge computing. Computer 50, 1 (2017), 30–39.Google ScholarDigital Library
- D. Schel, C. Henkel, D. Stock, O. Meyer, G. Rauhöft, P. Einberger, M. Stöhr, M. A. Daxer, and J. Seidelmann. 2018. Manufacturing service bus: An implementation. Proc. CIRP 67 (2018), 179–184.Google ScholarCross Ref
- K. Schweichhart. 2019. RAMI 4.0 Reference Architectural Model for Industrie 4.0. Retrieved from https://ec.europa.eu/futurium/en/system/files/ged/a2-schweichhart-reference_architectural_model_industrie_4.0_rami_4.0.pdf.Google Scholar
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge computing: Vision and challenges. IEEE IoT J. 3, 5 (2016), 637–646.Google Scholar
- H. R. Siller, D. Romero, R. J. Rabelo, and E. Vazquez. 2018. Advanced CPS service oriented architecture for smart injection molding and mods 4.0. In Proceedings of the International Conference on Intelligent Systems, Vol. 4. 428–434.Google Scholar
- F. Simetinger and Z. J. Zhang. 2020. Deriving secondary traits of industry 4.0: A comparative analysis of significant maturity models. Syst. Res. Behav. Sci. (2020), 1–16.Google Scholar
- K. Sipsas, K. Alexopoulos, V. Xanthakis, and G. Chryssolouris. 2016. Collaborative maintenance in flow-line manufacturing environments: An industry 4.0 approach. In Proceedings of the 5th CIRP Global Web Conference Research and Innovation for Future Production. 236–241.Google Scholar
- J. Stark. 2015. Product Lifecycle Management (Volume 1): 21st Century Paradigm for Product Realisation. Springer International.Google Scholar
- M. M. Strljic, T. Korb, T. Tasci, E. F. Tinsel, D. Pawlowicz, O. Riedel, and A. Lechler. 2018. A platform-independent communication framework for the simplified development of shop-floor applications as microservice components. In Proceedings of the IEEE International Conference on Advanced Manufacturing (ICAM’18). 250–253.Google Scholar
- H. Tang, D. Li, J. Wan, M. Imran, and M. Shoaib. 2020. A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence. IEEE IoT J. 7, 5 (2020), 4248–4259.Google Scholar
- F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui. 2018. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manufact. Technol. 94, 9–12 (2018), 3563–3576.Google ScholarCross Ref
- F. Tao, H. Zhang, A. Liu, and A. Y.C. Nee. 2019. Digital twin in industry: State-of-the-art. IEEE Trans. Industr. Inf. 15, 4 (2019), 2405–2415.Google ScholarCross Ref
- A. Theorin, K. Bengtsson, J. Provost, M. Lieder, C. Johnsson, T. Lundholm, and B. Lennartson. 2017. An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res. 55, 5 (2017), 1297–1311.Google ScholarCross Ref
- K. Thramboulidis, D. C. Vachtsevanou, and I. Kontou. 2019. CPuS-IoT: A cyber-physical microservice and IoT-based framework for manufacturing assembly systems. Annu. Rev. Contr. 47 (2019), 237–248.Google ScholarCross Ref
- V. M. Tovarnitchi. 2019. Designing distributed, scalable and extensible system using reactive architectures. In Proceedings of the 22nd International Conference on Control Systems and Computer Science (CSCS’19). 484–488.Google ScholarCross Ref
- S. Trabesinger, R. Pichler, D. Schall, and R. Gfrerer. 2019. Connectivity as a prior challenge in establishing CPPS on basis of heterogeneous IT-software environments. Proc. Manufact. 31 (2019), 370–376.Google ScholarCross Ref
- H.-L. Truong. 2018. Integrated analytics for IIoT predictive maintenance using IoT big data cloud systems. In Proceedings of the 2018 IEEE International Conference on Industrial Internet (ICII18). 109–118.Google ScholarCross Ref
- T. Usländer and U. Epple. 2015. Reference model of Industrie 4.0 service architectures: Basic concepts and approach. Automatisierungstechnik 63, 10 (2015), 858–866.Google ScholarCross Ref
- M. Villari, M. Fazio, S. Dustdar, O. Rana, and R. Ranjan. 2016. Osmotic computing: A new paradigm for edge/cloud integration. IEEE Cloud Comput. 3, 6 (2016), 76–83.Google ScholarCross Ref
- T. Vresk and I. Cavrak. 2016. Architecture of an interoperable IoT platform based on microservices. In Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO’16). 1196–1201.Google Scholar
- W. Wang, L. Fan, P. Huang, and H. Li. 2019. A new data processing architecture for multi-scenario applications in aviation manufacturing. IEEE Access 7 (2019), 83637–83650.Google ScholarCross Ref
- M. Wooldridge. 2009. An Introduction to Multiagent Systems (2nd ed.). Wiley.Google ScholarDigital Library
- M. Wu, X. Ding, and R. Hou. 2019. Design and implementation of B2B E-commerce platform based on microservices architecture. In Proceedings of the 2nd International Conference on Computer Science and Software Engineering (CSSE’19). 30–34.Google Scholar
- L. D. Xu, E. L. Xu, and L. Li. 2018. Industry 4.0: State of the art and future trends. Int. J. Prod. Res. 56, 8 (2018), 2941–2962.Google ScholarCross Ref
- L. Yang, W. Li, Y. Luo, Y. Duan, and G. Fortino. 2017. A social-D2D architecture for people-centric industrial Internet of Things. In Proceedings of the IEEE 14th International Conference on Networking, Sensing and Control (ICNSC’17). 744–749.Google Scholar
- L. Yue and X. Li. 2018. A smart manufacturing compliance architecture of electronic batch recording system (eBRS) for life sciences industry. In Proceedings of the 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE’18). 206–212.Google Scholar
- H. Zheng, Y. Feng, Y. Gao, and J. Tan. 2018. A robust predicted performance analysis approach for data-driven product development in the industrial internet of things. Sensors (Switz.) 18 (2018), 1–16.Google Scholar
- T. Zhu, S. Dhelim, Z. Zhou, S. Yang, and H. Ning. 2017. An architecture for aggregating information from distributed data nodes for industrial internet of things. Comput. Electr. Eng. 58 (2017), 337–349.Google ScholarDigital Library
- O. Zimmermann. 2017. Microservices tenets: Agile approach to service development and deployment. Comput. Sci. Res. Dev. 32, 3–4 (Jul. 2017), 301–310.Google ScholarDigital Library
Index Terms
- Service Computing for Industry 4.0: State of the Art, Challenges, and Research Opportunities
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