Revista UIS Ingenierías

Vol. 23 Núm. 2 (2024): Revista UIS Ingenierías
Artículos

El internet de las cosas y la industria 4.0- Aplicaciones en el campo de la ingeniería industrial

PDF
  • Reina Verónica Román-Salinas ,
  • Marco Antonio Díaz-Martínez ,
  • Santos Ruíz-Hernández,
  • Gabriela Cervantes-Zubirías ,
  • Mario Alberto Morales-Rodríguez
Reina Verónica Román-Salinas
TecNM- Instituto Tecnológico Superior de Pánuco
Marco Antonio Díaz-Martínez
TecNM- Instituto Tecnológico Superior de Pánuco
Santos Ruíz-Hernández
TecNM- Instituto Tecnológico Superior de Pánuco
Gabriela Cervantes-Zubirías
Universidad Autónoma de Tamaulipas Reynosa-Aztlán
Mario Alberto Morales-Rodríguez
Universidad Autónoma de Tamaulipas Reynosa-Aztlán
DOI: https://doi.org/10.18273/revuin.v23n2-2024007

Publicado 2024-06-26

Palabras clave

  • internet de las cosas,
  • industria 4.0,
  • ingeniería industrial,
  • Big Data,
  • aprendizaje automático,
  • simulación de procesos,
  • computación en la nube
    • ...Más
    Menos

Cómo citar

Román-Salinas , R. V. ., Díaz-Martínez , M. A., Ruíz-Hernández, S. ., Cervantes-Zubirías , G. ., & Morales-Rodríguez , M. A. . (2024). El internet de las cosas y la industria 4.0- Aplicaciones en el campo de la ingeniería industrial. Revista UIS Ingenierías, 23(2), 111–130. https://doi.org/10.18273/revuin.v23n2-2024007

Derechos de autor 2024 Revista UIS Ingenierías

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-SinDerivadas 4.0.

Resumen

Esta investigación tiene por objetivo realizar una revisión sistemática de la literatura para conocer las aplicaciones del Internet de las cosas y la industria 4.0 en la ingeniería industrial. Se hizo una revisión de la literatura en artículos científicos, obtenidos de bases de datos de EBSCO Essential, MDPI, ScienceDirect incluyendo IEEE Xplore. A partir de esta revisión se puede conocer la importancia del internet de las cosas asociada a la industria 4.0 y su relación con la ingeniería industrial. El presente articulo recopila diferentes aportes científicos en campos como machine learning, robótica, manufactura, simulación, etc., para describir la relevancia y hallazgos en el campo de la ingeniería industrial.

PDF

Descargas

Los datos de descargas todavía no están disponibles.

Referencias

  1. C. Ynzunza, J. Izar, J. Bocarando, “Implications and Perspectives of Industry 4.0”, Conciencia Tecnológica, vol. 1, no. 8, pp. 33-45, 2017.
  2. M. Xu, J. M. David, S. H. Kim, ‘‘The fourth industrial revolution: Opportunities and challenges,’’ Int. J. Financial Res., vol. 9, no. 2, pp. 92–95, 2018, doi: https://doi.org/10.5430/ijfr.v9n2p90
  3. J. Corrales, N. Ribeiro, D. Roque, “Las competencias exigidas a los trabajadores de la Industria 4.0: Cambios en la gestión de personas”, Relaciones Laborales, vol. 40, no. 1, pp. 161-184, 2022, doi: https://doi.org/10.5209/crla.72383
  4. M. Sujatha et al., “IoT and Machine Learning-Based Smart Automation System for Industry 4.0 Using Robotics and Sensors”, Journal of Nanomaterials, 2022, pp. 1-6, doi: https://doi.org/10.1155/2022/6807585
  5. G. Lăzăroiu, T. Kliestik, A. Novak, “Internet of things smart devices, industrial arti ficial intelligence, and real-time sensor networks in sustainable cyber-physical production systems”, Journal of Self-Governance and Management Economics, vol. 9, no. 1, pp. 20–30, 2021, doi: https://doi.org/10.22381/jsme9120212
  6. D. Velásquez et al., “A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems”, in IEEE Access, vol. 10, pp. 72024 - 72036, 2022, doi: https://doi.org/10.1109/ACCESS.2021.3188102
  7. G. Tancredi, G. Vignali, L. Bottani, “Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An application to a Food Plant”, Sensors, vol. 22, pp. 1-23, 2022, doi: https://doi.org/10.3390/s22114143
  8. A. Efimova, P. Briš, “The Implementation of the Conjunction of Lean Six Sigma and Industry 4.0: A case Study in the Czech Republic”, Sciendo, vol. 30, no. 3, pp. 223-229, 2022, doi: https://doi.org/10.2478/mspe-2021-0028
  9. H. Sikandar et al., “Scientific Mapping of Industry 4.0 Research: A bibliometric Analysis”, iJIM, vol. 15, no. 18, pp. 129-148, 2021, doi: https://doi.org/10.3991/ijim.v15i18.25535
  10. I. González,R. Granillo, “Competences of industrial engineers in industry 4.0”, Revista electrónica de investigación educativa, vol. 22, No. 30, pp. 1-14, doi: https://doi.org/10.24320/redie.2020.22.e30.2750
  11. M. Drakaki et al., “Machine Learning and Deep Learning Based Methods Towards Industry 4.0 Predictive Maintenance in Induction Motors: A State of the Art Survey”, Journal of Industrial Engineering and Management, vol.15, no. 1, pp. 31-57, 2021, doi: http://dx.doi.org/10.3926/jiem.3597
  12. S. Mokhtari et al., “A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data”, Electronics, vol. 10, no. 407, pp. 1-13, 2021, doi: https://doi.org/10.3390/electronics10040407
  13. S. Chalichalamala, N. Govindan, R. Kasarupa, “Logistic regression ensemble classifier for intrusion detection system in internet of things,” Sensors, vol. 23, no. 23, pp. 1–19, 2023, doi: https://doi.org/10.3390/s23239583
  14. M. Haider, M. Khan, H. Alkhalefah, “Predictive Maintenance Planning for Industry 4.0 Using Maching Learning for Sustainable Manufacturing”, Sustainability, vol. 14, no. 3387, pp. 1-27, 2022, doi: https://doi.org/10.3390/su14063387
  15. L. Yu, H. Ming, C. Ching, “A multiplier-free convolution neural network hardware accelerator for real-time bearing condition detection of CNC machinery,” Sensors, vol. 23, no. 23, pp. 1–19, 2023, doi: https://doi.org/10.3390/s23239437
  16. K. Masani, S. Agrawal, P. Oza, “Predictive maintenance and monitoring of industrial machine using machine learning”, Scalable Computing: Practice and Experience, vol.20, no. 4, pp. 663-667, 2019, doi: https://doi.org/10.12694/scpe.v20i4.1585
  17. E. Cumbajin, N. Rodrigues, P. Costa, R. Miragaia, L. Frazão, N. Costa, A. Fernández, J. Carneiro, L. Buruberri, A. Pereira, “A real-time automated defect detection system for ceramic pieces manufacturing process based on computer vision with deep learning,” Sensors, vol. 24, no. 1, pp. 1–22, 2024, doi: https://www.mdpi.com/1424-8220/24/1/232
  18. F. Ferras, R. Francelin, S. Jen, “Machine learning for detection and diagnostics of anomalies in applications driven by electric motors,” Sensors, vol. 23, no. 24, pp. 1–25, 2023, doi: https://doi.org/10.3390/s23249725
  19. G. Shankarrao, A. Pandya, “How artificial intelligence and machine learning assist in industry 4.0 for mechanical engineers”, Materialstoday Proceedings, 2022, doi: https://doi.org/10.1016/j.matpr.2022.08.201
  20. A. Jaramillo, J. Govea, W. Villegas, ‘‘Anomaly detection in a smart industrial machinery plant using IoT and machine learning,’’ Sensors, vol. 23, no. 19, pp. 1–22, 2023, doi: https://doi.org/10.3390/s23198286
  21. L. Concetti, G. Mazzuto, F. E. Ciarapica, M. Bevilacqua, “An unsupervised anomaly detection based on self-organizing map for the oil and gas sector,” Appl. sciences, vol. 13, no. 6, pp. 1–28, 2023, doi: https://doi.org/10.3390/app13063725
  22. I. Peinado, N. Montés, E. Garcia, “Virtual sensor of gravity centres for real-time condition monitoring of an industrial stamping press in the automotive industry,” Sensors, vol. 23, no. 14, pp. 1–17, 2023, doi: https://doi.org/10.3390/s23146569
  23. K. Tai, B. Gupta, J. Liu, V. Arya, N. Nedjah, A. Almomani, P. Chaurasia, “A survey of internet of things and cyber-physical system: standards, algorithms, applications, security, challenges, and future directions,” Information, vol. 14, no. 7, pp. 1–20, 2023, doi: https://www.mdpi.com/2078-2489/14/7/388
  24. M. Lowe, R. Qin, X. Mao, “A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring”, Water, vol. 14, no. 1384, 2022, doi: https://doi.org/10.3390/w14091384
  25. M. Guida, F. Caniato, A. Moretto, S. Ronchi, “The role of artificial intelligence in the procurement process: State of the art and research agenda,” Journal Of Purchasing and Supply Management, vol. 29, no. 2, pp. 1–21, 2023, doi: https://doi.org/10.1016/j.pursup.2023.100823
  26. F. Bachinger, J. Zenisek, M. Affenzeller, “Automated machine learning for industrial applications- challenges and opportunities,” Procedia Computer Science, vol. 232, pp. 1701–1710, 2024, doi: https://doi.org/10.1016/j.procs.2024.01.168
  27. S. Elkateb, A. Métwalli, A. Shendy, A. Abu, “Machine learning and IoT-Based predictive maintenance approach for industrial applications,” Alexandria Engineering Journal, vol. 88, pp. 298–309, 2024, doi: https://doi.org/10.1016/j.aej.2023.12.065
  28. A. Adam et al., “Green supply chain managament and performance of listed oil and gas firms in Nigeria: A moderating role of internet of thing”, Gusan Journal of Accounting and Finance, vol. 2, no. 2, 2021, pp. 1-23, doi: https://doi.org/10.57233/gujaf.v2i2.61
  29. K. Qureshi, B. Mewada, S. Kaur, S. Alghamdi, N. Almakayeel, A. Almuflih, M. Mohamed, “Sustainable manufacturing supply chain performance enhancement through technology utilization and process innovation in industry 4.0: A SEM-PLS approach,” Sustainability, vol. 15, no. 21, pp. 1–20, 2023, doi: https://doi.org/10.3390/su152115388
  30. A. Althabatah, M. Yaqot, B. Menezes, L. Kerbache, “Transformative procurement trends: Integrating industry 4.0 technologies for enhanced procurement processes,” Logistics, vol. 7, no. 3, pp. 1–40, 2023, doi: https://doi.org/10.3390/logistics7030063
  31. P. Renna, “A review of game theory models to support production planning, scheduling, cloud manufacturing and sustainable production systems,” Designs, vol. 8, no. 2, pp. 1–40, 2024, doi: https://doi.org/10.3390/designs8020026
  32. N. Adamashvili, N. Zhizhilashvili, C. Tricase, “The integration of the internet of things, artificial intelligence, and blockchain technology for advancing the wine supply chain,” Computers, vol. 13, no. 3, pp. 1–25, 2024, doi: https://www.mdpi.com/2073-431X/13/3/72
  33. N. Azizi et al., “IoT-Blockchain: Harnessing the Power of Internet of Thing and Blockchain for Smart Supply Chain,” Sensors, no. 21, 2021, pp. 1-25, doi: https://doi.org/10.3390/s21186048
  34. K. Pantanjal, S. Dheeraj, P. Pandey, “Industry 4.0 (I4.0) Based Virtual Organization Model for the Coordination of Sustainable Textile Supply Chain”, American Business Review, vol. 25, no. 1, 2022, doi: https://doi.org/10.37625/abr.25.1.186-208
  35. E. Elif, “Industry 4.0 and Sustainable Supply Chain”, Journal of Economic & Administrative Sciences, vol. 43, no. 1, pp. 123-144, 2021, doi: https://doi.org/10.14780/muiibd.960306
  36. A. Z. Musaddak et al., “Internet of Things-Based Smart and Connected Supply Chain: A Review”, International Journal of Antennas & Propagation, pp. 1-5, 2022, doi: https://doi.org/10.1155/2022/8182813
  37. L. Zhang, W. Zhou, J. Xia et al., “Dqn-based mobile edge computing for smart internet of vehicle”, EURASIP Journal on Advances in Signal Processing, no. 1, pp. 1–16, 2022, doi: https://doi.org/10.1186/s13634-022-00876-1
  38. T. de Vass, H. Shee, S. Miah, “The effect of -Internet of Things- on supply chain integration and performance: An organisational capability perspective,” Australasian Journal of Information Systems, vol. 22, pp. 1–30, 2018, doi: https://doi.org/10.3127/ajis.v22i0.1734
  39. H. Nazir, J. Fan, ‘‘Revolutionizing retail: Examining the influence of blockchain-enable IoT capabilities on sustainable firm performance,” Sustainability, vol. 16, no. 9, pp. 1–23, 2024, doi: https://doi.org/10.3390/su16093534
  40. A. Alzahrani, M. Zubair, “Intelligent risk prediction system in IoT-Based supply chain management in logistics sector,” Electronics, vol. 12, no.13, pp. 1–24, 2023, doi: https://doi.org/10.3390/electronics12132760
  41. S. Turki, “Major emerging markets: A systematic literature review of benefits, use, challenges, and mitigation strategies in supply chain management,” Sustainability, vol. 15, no.20, pp. 1–30, 2023, doi: https://doi.org/10.3390/su152014811
  42. F. Yudi, T. Ming, I. Wahyuni, A. Lopes de sousa, C. Chiappetta, C. Foropon, “Cyber supply chain risk management and performance in industry 4.0 era: information system security practices in Malaysia,” Journal of Industrial and Production Engineering, vol. 40, no. 2, doi: https://doi.org/10.1080/21681015.2022.2116495
  43. M. Jameel, T. Masood, “Industry 4.0 driven green supply chain management in renewable energy sector: A critical systematic literature review,” Journal of Industrial and Production Engineering., vol. 16, no.19, 2023, doi: https://doi.org/10.3390/en16196977
  44. R. Mouly, M. Kilaru, “Sustainability in supply chain management: A case study of the Indian retailing industry,” Eng. Proc, vol. 59, no.1, pp. 1-11, 2023, doi: https://doi.org/10.3390/engproc2023059064
  45. L. Lynberg, and A. Deif, “Network effects in blockchain and supply chain: a theorical research synthesis,” Modern Supply Chain Research and Applications, vol. 5, no.1, pp. 1-26, 2023, doi: https://doi.org/10.1108/MSCRA-07-2022-0016
  46. R. Mashat, S. Abourokbah, M. Asif, “Impact of internet of things adoption on organizational performance: A mediating analysis of supply chain integration, performance, and competitive advantage,” Sustainability, vol. 16, no.6, pp. 1-25, 2024, doi: https://doi.org/10.3390/su16062250
  47. P. Trakadas et al., “An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Externsions and Potential Applications”, Sensors, vol. 20, no. 5480, pp. 1-20, 2020, doi: https://doi.org/10.3390/s20195480
  48. W. Qilin, Z. Yue, “Automation Design and Organization Innovation of Manufacturing Enterprises Based on the Internet of Things”, Hindawi Scientific Programming, pp. 1–12, 2022, doi: https://doi.org/10.1155/2022/8729731
  49. L. Liu, P. Zhao, “Manufacturing Service Innovation and Foreign Trade Upgrade Model Based on Internet of Things and Industry 4.0”, Hindawi Mathematical Problems in Engineering, pp. 1–13, 2022, doi: https://doi.org/10.1155/2022/4148713
  50. P. Barrios, C. Danjou B. Eynard, “Literature review and methodological framework fo integration of IoT and PLM in manufacturing industry”, Computers in Industry, vol. 140, 2022, doi: https://doi.org/10.1016/j.compind.2022.103688
  51. M. Sira, “Efficient Practices of Cognitive Technology Application For Smart Manufacturing”, Sciendo-Management Systems in Production Engineering, vol. 30, no. 2, pp. 187-191, 2022, doi: https://doi.org/10.2478/mspe-2022-0023
  52. A. Reddy, “Effective usage of artificial intelligence in enterprise resource planning applications,” International Journal Of Computer Trends And Technology, vol. 71, no.4, pp. 73-80, 2023, doi: https://doi.org/10.14445/22312803/IJCTT-V71I4P109
  53. K. Vukman, K. Klarić, K. Greger, I. Perić, “Driving efficiency and competitiveness: Trends and innovations in ERP systems for wood industry,” Forests, vol. 15, no.2, pp. 1-21, 2024, doi: https://doi.org/10.3390/f15020230
  54. N. Kashpruk, C. Piskor, J. Baranowski, “Time series prediction in industry 4.0: A comprehensive review and prospect for future advancements,” Appl. Scien, vol. 13, no.22, pp. 1-20, 2023, doi: https://doi.org/10.3390/app132212374
  55. C. Serôdio, P. Mestre, J. Cabral, M. Gomes, F. Branco, “Software and architecture orchestration for process control in industry 4.0 enable by cyber-physical systems technologies,” Appl. Scien., vol. 14, no.5, pp. 1-17, 2024, doi: https://doi.org/10.3390/app14052160
  56. M. Ullah et al., “Industrial Energy Management System: Design of a Conceptual Framework Using IoT and Big Data”, TechRxiv, vol. 10, pp. 110557-110567, 2021, doi: https://doi.org/10.36227/techrxiv.17045891.v1
  57. AL. Fadi, BD. Deeback, “Seamless Authentication: For IoT-Big Data Technologies in Smart Industrial Application Systems”, IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2919-2927, 2021, doi: https://doi.org/10.1109/TII.2020.2990741
  58. P. Pathak, N. Vyas, S. Joshi, “Security Challenges for Communications on IOT & Big Data”, International Journal of Advanced Research in Computer Science, vol. 8, no. 3, pp. 431-436, 2017.
  59. D. Andersen, C. Ashbrook, N. Karlborg “Significance of Big Data analytics and internet of things (IoT) aspects in industrial development, governance and sustainability”, International Journal of Intelligence Networks, Vol. 1, pp. 107-111, 2020, Available: https://doi.org/10.1016/j.ijin.2020.12.003
  60. L. Hu, X. Xia, “5G-Oriented IoT Big Data Analysis Method System”, Hindawi Mobile Information Systems, pp. 1–9, 2021, doi: https://doi.org/10.1155/2021/3186696
  61. L. Pirrone, A. Bionda, A. Ratti, “How digital technologies can support sustainability of the waterborne passenger mobility ecosystem: A case study analysis of smart circular practice in northern Europe,” Sustainability, vol. 16, no.1, pp. 1-21, 2024, doi: https://doi.org/10.3390/su16010353
  62. K. Suann, C. Min, J. Shyong, T. Hung, Y. Syuan, ‘‘Fuzzy radar evaluation chart for improving machining quality of components,” Mathematics., vol. 12, no.5, pp. 1-17, 2024, doi: https://doi.org/10.3390/math12050732
  63. Y. Saif, A. Zafiah, Y. Yusof, M. Lliyas, S. Al, D. Hissein, A. Adam, Y. Hyeon, M. Al, H. Abdullah, “Advancements in roundness measurement parts for industrial automation using internet of things architecture-based computer vision and image processing techniques,” Appl. Scien., vol. 13, no.20, pp. 1-22, 2023, doi: https://doi.org/10.3390/app132011419
  64. L. Ortíz et al., “Computación en la Nube: Estudio de Herramientas Orientadas a la Industria 4.0”, Lámpsakos, no. 20, pp. 68–75, 2018, doi: https://doi.org/10.21501/21454086.2560
  65. I. Abdullahi, S. Longo, M. Samie, “Towards a distributed digital twin framework for predictive maintenance in industrial internet of things (IIoT),” Sensors, vol. 24, no.8, pp. 1-30, 2024, doi: https://doi.org/10.3390/s24082663
  66. E. Taiwo, O. Christiana, J. Bamiele, “A lightweight image cryptosystem for cloud-assisted internet of things,” Applied Scie., vol. 14, no.7, pp. 1-27, 2024, doi: https://doi.org/10.3390/app14072808
  67. R.M.M. Salem et al., “An Industrial Cloud-Based IoT System for Real-Time Monitoring and Controlling of Wastewater”, IEEE Access, vol. 10, pp. 6528 – 6540, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3141977
  68. N. Sun, “Deep Learning System for Recycled Clothing Classification Linked to Cloud and Edge Computing”, Hindawi Computational Intelligence and Neuroscience, 2022, doi: https://doi.org/10.1155/2022/6854626
  69. J. Nam, Y. Yun, M. Choi, “High Performance IoT Cloud Computing Framework Using Pub/Sub Techniques”, Applied Sciences, vol. 12, no. 11009, pp. 1-20, 2022, doi: https://doi.org/10.3390/app122111009
  70. L. Song, “Construction of Accounting Internal Control Managament Platform Based on IoT Cloud Computing”, Hindawi- Wireless Communications and Mbile Computings, vol. 2022, pp. 1-13, 2022, doi: https://doi.org/10.1155/2022/9552118
  71. S. Badotra, S. Panda, “A review on software-defined networking enabled IoT cloud computing”, IIUM Engineering Journal, vol. 20, no. 2, 2019, doi: https://doi.org/10.31436/iiumej.v20i2.1130
  72. H. Li, “Implementation of chemical logistics supervision forewarning platform based on IoT cloud computing”, Chemical, vol.71, pp. 727-732, 2018, doi: https://doi.org/10.3303/CET1871122
  73. S. Janík, M. Míkva, M. Marecêk, “Effective data utilization in the context of industry 4.0 technology integration,” Appl. Sci, vol. 12, no. 10517, pp. 1-16, 2022, doi: https://doi.org/10.3390/app122010517
  74. Y. Hung, “Investigating how the cloud computing transforms the development of industries”, IEEE access, no. 7, pp.181505-181517, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2958973
  75. C. Cacciuttolo, V. Guzmán, P. Catriñir, E. Atencio, “Sensor technologies for safety monitoring in mine tailings storage facilities: Solutions in the industry 4.0 Era,” Minerals, vol. 14, no. 5, pp. 1-34, 2024, doi: https://doi.org/10.3390/min14050446
  76. M. Zafar, A. Alsabban, “Industry-4.0-Enable digital transformation: Prospects, instruments, challenges, and implications for business state,” Sustainability, vol. 15, no. 11, pp. 1-33, 2023, doi: https://doi.org/10.3390/su15118553
  77. X. Cao, H. Bo, Y. Liu, X. Liu, “Effects of different resource-sharing strategies in cloud manufacturing: a Stackelberg game-based approach,” International Journal Of Production Research, vol. 61, no. 2, pp. 520-540, 2023, doi: https://doi.org/10.1080/00207543.2021.2010824
  78. W. Zhu, C. Zhou, L. Jiang, “A trusted internet of things access scheme for cloud edge collaboration,” Electronics, vol. 13, no. 6, pp. 1-17, 2024, doi: https://doi.org/10.3390/electronics13061026
  79. J. Khan et al., “Artificial intelligence and internet of things (AI-IoT) technologies in response to covid-19 pandemic: A systematic review”, IEEE Xplore, vol. 10, pp. 62613-62660, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3181605
  80. C. Naves, F. Verdier, S. Glock, P. Guitton, “A fair crowd-sourced automotive data monetization approach using substrate hybrid consensus blockchain,” Future Internet., vol. 16, no. 5, pp. 1-27, 2024, doi: https://doi.org/10.3390/fi16050156
  81. I. Leila, R. Buyya, “Artificial intelligence applications and self-Learning 6G networks for smart cities digital ecosystems: taxonomy, challenges, and future directions”, Sensors, vol. 22, no. 15, pp. 2-30, 2022, doi: https://doi.org/10.3390/s22155750
  82. D. Stadnicka et al., “Industrial needs in the fields of artificial intelligence, internet of things and edge computing”, Sensors, no. 22, 2022, pp. 1-47, doi: https://doi.org/10.3390/s22124501
  83. L. Pa´sko et al., “Plan and develop advanced knowledge and kkills for future industrial employees in the field of artificial intelligence, internet of things and edge computing”, Sustainability, vol.14, no. 6, pp. 1-49, 2022, doi: https://doi.org/10.3390/ su14063312
  84. L. Lachvajderová, J. Kádárová, “Industry 4.0 implementation and industry 5.0 readliness in industrial enterprises”, Management And Production Engineering Review, vol. 13, no. 3, pp. 102-109, 2022, doi: https://doi.org/10.24425/mper.2022.142387
  85. A. Sayeed et al., “Approaches and challenges in internet of robotics things”, Future internet, vol. 14, no. 265, pp. 1-30, 2022, doi: https://doi.org/10.3390/fi14090265
  86. T. Ahmad et al., “Energetics systems and artificial intelligence: applications of industry 4.0”, Energy Reports, 2022, vol. 8, pp. 334-361, doi: https://doi.org/10.1016/j.egyr.2021.11.256
  87. A. Toopshekan, H. Yousefi and F.R. Astaraei, “Technical, economic, and performance analysis of hybrid energy system using a novel dispatch strategy” Energy, 2020, vol. 213, pp. 1-19, doi: https://doi.org/10.1016/j.energy.2020.118850
  88. P. Mah, I. Skalna, J. Muzam, “Natural languaje processing and artificcial intelligence for enterprise managament in the era of industry 4.0”, Applied Scienses, vol. 12, no. 9207, pp. 1-26, 2022, doi: https://doi.org/10.3390/app12189207
  89. M. Tanniru et al., “An agile digital platform to support population health- A case study of a digital platform to support patients with delirium using IoT, NLP, and IA”, International Journal of Environment Research and Public Health, vol. 18, no. 5686, pp. 1-22, 2021, doi: https://doi.org/10.3390/ijerph18115686
  90. H. Nasreddine et al., “AIoT with I4.0: the effect of internet of things and artificial intelligence technologies on the industry 4.0”, ITM Web of Conference, vol. 46, pp. 1-5, 2022, doi: https://doi.org/10.1051/itmconf/20224603002
  91. W. Rafique et al., “An Application Development Framework for Internet of things service orchestration,” IEEE Internet Of Things Journal, vol. 7, No. 5, pp. 4543-4556, 2020, doi: https://doi.org/10.1109/JIOT.2020.2971013
  92. D. De Avila et al., “Internet of things e inteligência artificial nos meios produtivos”, Revista CIATEC, vol. 14, No. 2, pp. 156-165, 2022, doi: https://doi.org/10.5335/ciatec.v14i2.13789
  93. P. Araújo, R. Moreira, L. Veiga, L. Dos Santos, V. Batista, “Artificial intelligence and industry 4.0? Validation of challenges considering the context of an emerging economy country using Cronbach’s Alpha and the Lawshe method,” Eng., vol. 4, no. 3, pp. 1-16, 2023, doi: https://doi.org/10.3390/eng4030133
  94. J. Li, M. Sacit, J. Nathwani, J. Wen, “Methods and applications for artificial intelligence, Big Data, Internet of things, and blockchain in smart energy management,” Energy and Ai., vol. 11, pp. 1-18, 2023, doi: https://doi.org/10.1016/j.egyai.2022.100208
  95. L. Espina, J. Gregorio, H. Gutiérrez, H. Dworaczek, Y. Solier, L. Emérita, J. Rio, “Which industrial sectors are affected by artificial intelligence? A bibliometric analysis of trends and perspective,” Sustainability, vol. 15, no. 16, pp. 1-18, 2023, doi: https://doi.org/10.3390/su151612176
  96. I. Rojek et al., “Modern methods in the field of machine modelling and simulation as a research and practical issue related to industry 4.0”, Bull. Pol. Acad. Sci. Tech. Sci, Vol. 69, no. 2, pp. 1-12, 2021, doi. https://doi.org/10.24425/bpasts.2021.136717
  97. G. Jin, S. Ma, Z. Li, “Dynamic simulation modeling of industrial robot kinematics in industry 4.0”, Hindawi- Discrete dynamics in nature and society, 2022, vol. 2022, pp. 1-11, doi: https://doi.org/10.1155/2022/3217360
  98. B. Rodič, “Industry 4.0 and the new simulation modelling paradigm”, Organizacija, vol. 50, no. 3, pp. 193-207, 2022, doi: https://doi.org/10.1515/orga-2017-0017
  99. Y. Zêdo, A. Andrade, "Simulation for decision support in process reengineering in the automotive industry", International Journal of Mathematical, Engineering and Management Sciences, vol. 7, no. 2, pp. 176-195, 2022, doi: https://doi.org/10.33889/IJMEMS.2022.7.2.012
  100. S. Luściński, V. Ivanov, “A simulation study of industry 4.0 factories based on the ontology on flexibility with using Flexsim software”, Management and Production Engineering Review, vol. 11, no. 3, pp. 74-83, 2020, doi: https://doi.org/10.24425/mper.2020.134934
  101. M. Niekurzak, E. Kubińska, “Production line modelling in accordance with the industry 4.0 concept as an element of process management in the iron and steel industry”, Management and Production Engineering Review, vol. 12, no. 4, pp. 3-12, 2021, doi: https://doi.org/10.24425/mper.2021.139990
  102. J. Cavata et al., “Highlighting the benefits of industry 4.0 for production: an agent-based simulation approach”, Gestão & Produção, vol. 27, no. 3, pp. 1-35, 2020, doi: https://doi.org/10.1590/0104-530X5619-20
  103. L. Alpala et al., “Methodology for the design and simulation of industrial facilities and production systems based on a modular approach in an industry 4.0 context”, Dyna, vol. 85, no. 207, pp. 243-252, 2018, doi: http://doi.org/10.15446/dyna.v85n207.68545
  104. A. Florescu, S. Barabas, “Modeling and simulation of a flexible manufacturing system—a basic component of industry 4.0”, Applied Sciences, vol. 10, no. 22, pp. 1-20, 2020, doi: http://dx.doi.org/10.3390/app10228300
  105. M. Arucu, “Industry 4.0 – Holistic Perspective: Modeling and Simulation Implementations in Manufacturing Systems”, Journal of Innovative Science And Engineering, vol. 5, no. 1, pp. 50-63, 2021, doi: https://doi.org/10.38088/jise.816023
  106. J. Braun et al., “A robot localization propostal the robotat factory 4.0: A novel robotics competition within the industry 4.0 concept”, Frontiers, vol. 9, pp. 1-19, 2022, doi: https://doi.org/10.3389/frobt.2022.1023590
  107. K. Klincewicz, “Robotics in the context of industry 4.0: patenting activities in Poland and their comparison with global developments”, Problemy ZarzÈdzania, 2019, vol.17, pp. 53-95, doi: https://doi.org/10.7172/1644-9584.82.3
  108. H. Parmar et al., “Advanced robotics and additive manufacturing of composites: towards a new era in industry 4.0”, Materials and Manufacturing Processes, 2021, vol. 37, no. 5, pp. 483-517, doi: https://doi.org/10.1080/10426914.2020.1866195
  109. J. Ribeiro, “Robotic process automation and artificial inltelligence in industry 4.0- a literature review”, Procedia-Computer Science, vol. 181, pp. 51-58, 2021, doi: https://doi.org/10.1016/j.procs.2021.01.104
  110. J. Enríquez et al., “Robotic process automation: A scientific and industrial systematic mapping study”, IEEEaccess, 2020, vol. 8, pp. 39113-39129, doi: https://doi.org/10.1109/ACCESS.2020.2974934
  111. J. Yi, L. Guo, “AHP-Based network security situation assessment for industrial internet of things,” Electronics., vol. 12, no. 16, pp. 1-20, 2023, doi: https://www.mdpi.com/2079-9292/12/16/3458
  112. J. Zhang, H. Feng, B. Liu, D. Zhao, “Survey of technology in network security situation awareness,” Sensors, vol. 23, no. 5, pp. 1-25, 2023, doi: https://doi.org/10.3390/s23052608
  113. H. Alshahrani, A. Khan, M. Rizwan, M. Saleh, A. Sulaiman, A. Shaikh, “Intrusion detection framework for industrial internet of things software defined network,” Sustainability, vol. 23, no. 5, pp. 1-18, 2023, doi: https://doi.org/10.3390/su15119001
  114. T. Emad, C. Yung, S. Manickan, “Machine learning techniques to detect a DDoS attack in SDN: A systematic review,” Appl. Scie., vol. 13, no. 5, pp. 1-27, 2023, doi: https://doi.org/10.3390/app13053183
  115. S. Amertet, G. Gebresenbet, H. Mohammed, “Utilizing an internet of things (IoT) device, intelligent control design, and simulation for an agricultural system,” IoT., vol. 5, no. 1, pp. 1-21, 2024, doi: https://doi.org/10.3390/iot5010004
  116. H. Karimah, M. Akmal, K. Syazwan, N. Alina, A. González, J. Corchado, M. Saberi, “Recent advancements and chellenges of AIoT application in smart agriculture: A review,” IoT., vol. 23, no. 7, pp. 1-22, 2023, doi: https://doi.org/10.3390/s23073752
  117. K. Valaskova, M. Nagy, G. Grecu, “Digital Twin simulation modeling, artificial intelligence-based internet of manufacturing things systems, and virtual machine and cognitive computing algorithms in the industry 4.0 based Slovak labor market,” Oeconomia, vol. 15, no. 1, pp. 1-49, 2024, doi: https://doi.org/10.24136/oc.2814
  118. M. Lemstra, E. Quinaglia, M. de Mesquita, “Industry 4.0 Technologies in industrial engineering courses: A faculty survey in Brazil”, International Journal of Engineering Education, vol. 38, no. 5, pp. 1458-1469, 2022.
  119. S. Coşkun, Y. Kayıkcı, E. Gençay, “Adapting Engineering Education to Industry 4.0 Vision”, Technologies, vol. 7, no. 1, pp. 1-13, 2019, doi: http://dx.doi.org/10.3390/technologies7010010