Vol. 19 No. 2 (2024)

Articles

Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse

Published 30-06-2024

Tomas Kliestik
University of Zilina

Pavol Kral
University of Zilina

Martin Bugaj
University of Zilina

Pavol Durana
University of Zilina

Abstract

Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse.

Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse.

Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools.

Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

Keywords

generative artificial intelligence of things, multisensory immersive extended reality, big data, digital twin, industrial metaverse

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References

  1. Agarwal, A., & Alathur, S. (2023). Metaverse revolution and the digital transformation: Intersectional analysis of Industry 5.0. Transforming Government: People, Process and Policy, 17, 688‒707.
    View in Google Scholar DOI: https://doi.org/10.1108/TG-03-2023-0036
  2. Alimam, H., Mazzuto, G., Tozzi, N., Ciarapica, F. E., & Bevilacqua, M. (2023). The resurrection of digital triplet: A cognitive pillar of human‒machine integration at the dawn of Industry 5.0. Journal of King Saud University ‒ Computer and Information Sciences, 35(10), 101846.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jksuci.2023.101846
  3. Al-Sharafi, M. A., Al-Emran, M., Al-Qaysi, N., Iranmanesh, M., & Ibrahim, N. (2023). Drivers and barriers affecting metaverse adoption: A systematic review, theoretical framework, and avenues for future research. International Journal of Human–Computer Interaction.
    View in Google Scholar DOI: https://doi.org/10.1080/10447318.2023.2260984
  4. Aung, N., Dhelim, S., Chen, L., Ning, H., Atzori, L., & Kechadi, T. (2024). Edge-enabled metaverse: The convergence of metaverse and mobile edge computing. Tsinghua Science and Technology, 29(3), 795‒805.
    View in Google Scholar DOI: https://doi.org/10.26599/TST.2023.9010052
  5. Awan, K. A., Din, I. U., Almogren, A., & Seo-Kim, B. (2023). Blockchain-based trust management for virtual entities in the metaverse: A model for avatar and virtual organization interactions. IEEE Access, 11, 136370‒136394.
    View in Google Scholar DOI: https://doi.org/10.1109/ACCESS.2023.3337806
  6. Balaska, V., Adamidou, Z., Vryzas, Z., & Gasteratos, A. (2023). Sustainable crop protection via robotics and artificial intelligence solutions. Machines, 11(8), 774.
    View in Google Scholar DOI: https://doi.org/10.3390/machines11080774
  7. Bellalouna, F., & Puljiz, D. (2023). Use case for the application of the industrial metaverse approach for engineering design review. Procedia CIRP, 119, 638‒643.
    View in Google Scholar DOI: https://doi.org/10.1016/j.procir.2023.03.116
  8. Bhattacharya, P., Saraswat, D., Savaliya, D., Sanghavi, S., Verma, A., Sakariya, V., Sharma, R., Raboaca, M. S., & Manea, D. L. (2023). Towards future Internet: The metaverse perspective for diverse industrial applications. Mathematics, 11(4), 941.
    View in Google Scholar DOI: https://doi.org/10.3390/math11040941
  9. Calandra, D., Oppioli, M., Sadraei, R., Jafari-Sadeghi, V., & Biancone, P. P. (2024). Metaverse meets digital entrepreneurship: A practitioner-based qualitative synthesis. International Journal of Entrepreneurial Behavior & Research, 30(2/3), 666‒686.
    View in Google Scholar DOI: https://doi.org/10.1108/IJEBR-01-2023-0041
  10. Camacho-Muñoz, G. A., Camilo Martínez Franco, J., Nope-Rodríguez, S. E., Loaiza-Correa, H., Gil-Parga, S., & Álvarez-Martínez, D. (2023). 6D-ViCuT: Six degree-of-freedom visual cuboid tracking dataset for manual packing of cargo in warehouses. Data in Brief, 49, 109385.
    View in Google Scholar DOI: https://doi.org/10.1016/j.dib.2023.109385
  11. Cao, J., Zhu, X., Sun, S., Wei, Z., Jiang, Y., Wang, J., & Lau, V. K. N. (2023). Toward industrial metaverse: Age of information, latency and reliability of short-packet transmission in 6G. IEEE Wireless Communications, 30(2), 40‒47.
    View in Google Scholar DOI: https://doi.org/10.1109/MWC.2001.2200396
  12. Chai, T., Li, M., Zhou, Z., Cheng, S., Jia, Y., & Wu, Z. (2023a). An intelligent control method for the low-carbon operation of energy-intensive equipment. Engineering, 27, 84‒95.
    View in Google Scholar DOI: https://doi.org/10.1016/j.eng.2023.05.018
  13. Chai, Y., Qian, J., & Younas, M. (2023b). Metaverse: Concept, key technologies, and vision. International Journal of Crowd Science, 7(4), 149‒157.
    View in Google Scholar DOI: https://doi.org/10.26599/IJCS.2023.9100024
  14. Chang, L., Zhang, Z., Li, P., Xi, S., Guo, W., Shen, Y., Xiong, Z., Kang, J., Niyato, D., Qiao, X., & Wu, Y. (2022). 6G-enabled edge AI for metaverse: Challenges, methods, and future research directions. Journal of Communications and Information Networks, 7(2), 107‒121.
    View in Google Scholar DOI: https://doi.org/10.23919/JCIN.2022.9815195
  15. Chen, C., Fu, H., Zheng, Y., Tao, F., & Liu, Y. (2023a). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581‒594.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jmsy.2023.10.010
  16. Chen, C., Zhang, H., Hou, J., Zhang, Y., Zhang, H., Dai, J., Pang, S., & Wang, C. (2023b). Deep learning in the ubiquitous human–computer interactive 6G era: Applications, principles and prospects. Biomimetics, 8(4), 343.
    View in Google Scholar DOI: https://doi.org/10.3390/biomimetics8040343
  17. Chen, W., Zeng, C., Liang, H., Sun, F., & Zhang, J. (2023c). Multimodality driven impedance-based Sim2Real transfer learning for robotic multiple peg-in-hole assembly. IEEE Transactions on Cybernetics.
    View in Google Scholar DOI: https://doi.org/10.1109/TCYB.2023.3310505
  18. Chen, Y., Huang, W., Jiang, X., Zhang, T., Wang, Y., Yan, B., Wang, Z., Chen, Q., Xing, Y., Li, D., & Long, G. (2023d). UbiMeta: A ubiquitous operating system model for metaverse. International Journal of Crowd Science, 7(4), 180‒189.
    View in Google Scholar DOI: https://doi.org/10.26599/IJCS.2023.9100028
  19. Chowdhury, M. (2023a). Icon: An intelligent resource slicing and task coordination framework for Web 3.0 and metaverse-based service execution over 6G-based immersive edge computing network. International Journal of Ad Hoc and Ubiquitous Computing, 44(3), 167‒202.
    View in Google Scholar DOI: https://doi.org/10.1504/IJAHUC.2023.134763
  20. Chowdhury, M. (2023b). Servant: A user service requirements, timeslot sacrifice, and triple benefit-aware resource and worker provisioning scheme for digital twin and MEC enhanced 6G networks. International Journal of Sensor Networks, 41(4), 205‒228.
    View in Google Scholar DOI: https://doi.org/10.1504/IJSNET.2023.130710
  21. Cui, Z., Yang, X., Yue, J., Liu, X., Tao, W., Xia, Q., & Wu, C. (2023). A review of digital twin technology for electromechanical products: Evolution focus throughout key lifecycle phases. Journal of Manufacturing Systems, 70, 264‒287.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jmsy.2023.07.016
  22. Dolgui, A., & Ivanov, D. (2023). Metaverse supply chain and operations management. International Journal of Production Research, 61(23), 8179‒8191.
    View in Google Scholar DOI: https://doi.org/10.1080/00207543.2023.2240900
  23. Erman, B., & Martino, C. D. (2023). Generative network performance prediction with network digital twin. IEEE Network, 37(2), 286‒292.
    View in Google Scholar DOI: https://doi.org/10.1109/MNET.002.2200515
  24. Faraboschi, P., Frachtenberg, E., Laplante, P., Milojicic, D., & Saracco, R. (2023). Digital transformation: Lights and shadows. Computer, 56(4), 123‒130.
    View in Google Scholar DOI: https://doi.org/10.1109/MC.2023.3241726
  25. Ferrari, F., & McKelvey, F. (2023). Hyperproduction: A social theory of deep generative models. Distinktion: Journal of Social Theory, 24(2), 338‒360.
    View in Google Scholar DOI: https://doi.org/10.1080/1600910X.2022.2137546
  26. Ferrigno, G., Di Paola, N., Oguntegbe, K. F., & Kraus, S. (2023). Value creation in the metaverse age: A thematic analysis of press releases. International Journal of Entrepreneurial Behavior & Research, 29(11), 337‒363.
    View in Google Scholar DOI: https://doi.org/10.1108/IJEBR-01-2023-0039
  27. Fu, M., Wang, Z., Wang, J., Wang, Q., Wu, J., Sun, L., Ma, Z., Huang, R., Li, X., Wang, D., & Liang, Q. (2023). Environmental intelligent perception in the industrial Internet of Things: A case study analysis of a multicrane visual sorting system. IEEE Sensors Journal, 23(19), 22731‒22741.
    View in Google Scholar DOI: https://doi.org/10.1109/JSEN.2023.3294962
  28. Ganchev, I., Ji, Z., & O’Droma, M. (2023). Horizontal IoT platform EMULSION. Electronics, 12(8), 1864.
    View in Google Scholar DOI: https://doi.org/10.3390/electronics12081864
  29. Gattullo, M., Laviola, E., Evangelista, A., Fiorentino, M., & Uva, A. E. (2022). Towards the evaluation of augmented reality in the metaverse: Information presentation modes. Applied Sciences, 12(24), 12600.
    View in Google Scholar DOI: https://doi.org/10.3390/app122412600
  30. Gourisetti, S. N. G., Bhadra, S., Sebastian-Cardenas, D. J., Touhiduzzaman, M., & Ahmed, O. A. (2023). Theoretical open architecture framework and technology stack for digital twins in energy sector applications. Energies, 16(13), 4853.
    View in Google Scholar DOI: https://doi.org/10.3390/en16134853
  31. Grieves, M. (2023). Digital twin certified: Employing virtual testing of digital twins in manufacturing to ensure quality products. Machines, 11(8), 808.
    View in Google Scholar DOI: https://doi.org/10.3390/machines11080808
  32. Guo, Y., Klink, A., Bartolo, P., & Guo, W. G. (2023). Digital twins for electro-physical, chemical, and photonic processes. CIRP Annals, 72(2), 593‒619.
    View in Google Scholar DOI: https://doi.org/10.1016/j.cirp.2023.05.007
  33. Han, J., Yang, M., Chen, X., Liu, H., Wang, Y., Li, J., Su, Z., Li, Z., & Ma, X. (2023a). ParaDefender: A scenario-driven parallel system for defending metaverses. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2118‒2127.
    View in Google Scholar DOI: https://doi.org/10.1109/TSMC.2022.3228928
  34. Han, S., Jin, L., Xu, X., Tao, X., & Zhang, P. (2023b). R3C: Reliability and control cost co-aware in RIS-assisted wireless control systems for IIoT. IEEE Internet of Things Journal, 11(8), 13692‒13707.
    View in Google Scholar DOI: https://doi.org/10.1109/JIOT.2023.3338618
  35. Hou, J., Chen, G., Li, Z., He, W., Gu, S., Knoll, A., & Jiang, C. (2024). Hybrid residual multiexpert reinforcement learning for spatial scheduling of high-density parking lots. IEEE Transactions on Cybernetics, 54(5), 2771‒2783.
    View in Google Scholar DOI: https://doi.org/10.1109/TCYB.2023.3312647
  36. Hou, X., Wang, J., Jiang, C., Meng, Z., Chen, J., & Ren, Y. (2024). Efficient federated learning for metaverse via dynamic user selection, gradient quantization and resource allocation. IEEE Journal on Selected Areas in Communications, 42(4), 850‒866.
    View in Google Scholar DOI: https://doi.org/10.1109/JSAC.2023.3345393
  37. Huawei, H., Qinnan, Z., Taotao, L., Qinglin, Y., Zhaokang, Y., Junhao, W., Xiong, Z., Jianming, Z., Wu, J., & Zheng, Z. (2023). Economic systems in the metaverse: Basics, state of the art, and challenges. ACM Computing Surveys, 56(4), 99.
    View in Google Scholar DOI: https://doi.org/10.1145/3626315
  38. Jagatheesaperumal, S. K., & Rahouti, M. (2022). Building digital twins of cyber physical systems with metaverse for Industry 5.0 and beyond. IT Professional, 24(6), 34‒40.
    View in Google Scholar DOI: https://doi.org/10.1109/MITP.2022.3225064
  39. Jagatheesaperumal, S. K., Yang, Z., Yang, Q., Huang, C., Xu, W., Shikh-Bahaei, M., & Zhang, Z. (2023). Semantic-aware digital twin for metaverse: A comprehensive review. IEEE Wireless Communications, 30(4), 38‒46.
    View in Google Scholar DOI: https://doi.org/10.1109/MWC.003.2200616
  40. Jaimini, U., Zhang, T., Brikis, G. O., & Sheth, A. (2022). iMetaverseKG: Industrial metaverse knowledge graph to promote interoperability in design and engineering applications. IEEE Internet Computing, 26(6), 59‒67.
    View in Google Scholar DOI: https://doi.org/10.1109/MIC.2022.3212085
  41. Jamshidi, M., Dehghaniyan Serej, A., Jamshidi, A., & Moztarzadeh, O. (2023). The meta-metaverse: Ideation and future directions. Future Internet, 15(8), 252.
    View in Google Scholar DOI: https://doi.org/10.3390/fi15080252
  42. Ji, B., Wang, X., Liang, Z., Zhang, H., Xia, Q., Xie, L., Yan, H., Sun, F., Feng, H., Tao, K., Shen, Q., & Yin, E. (2023). Flexible strain sensor-based data glove for gesture interaction in the metaverse: A review. International Journal of Human–Computer Interaction.
    View in Google Scholar DOI: https://doi.org/10.1080/10447318.2023.2212232
  43. Jim, J. R., Hosain, M. T., Mridha, M. F., Kabir, M. M., & Shin, J. (2023). Toward trustworthy metaverse: Advancements and challenges. IEEE Access, 11, 118318‒118347.
    View in Google Scholar DOI: https://doi.org/10.1109/ACCESS.2023.3326258
  44. Kaarlela, T., Padrao, P., Pitkäaho, T., Pieskä, S., & Bobadilla, L. (2023a). Digital twins utilizing XR-technology as robotic training tools. Machines, 11(1), 13.
    View in Google Scholar DOI: https://doi.org/10.3390/machines11010013
  45. Kaarlela, T., Pitkäaho, T., Pieskä, S., Padrão, P., Bobadilla, L., Tikanmäki, M., Haavisto, T., Blanco Bataller, V., Laivuori, N., Luimula, M. (2023b). Towards metaverse: Utilizing extended reality and digital twins to control robotic systems. Actuators, 12(6), 219.
    View in Google Scholar DOI: https://doi.org/10.3390/act12060219
  46. Kaigom, E. G. (2024). Metarobotics for industry and society: Vision, technologies, and opportunities. IEEE Transactions on Industrial Informatics. 20(4), 5725‒5736.
    View in Google Scholar DOI: https://doi.org/10.1109/TII.2023.3337380
  47. Khalaj, O., Jamshidi, M., Hassas, P., Hosseininezhad, M., Mašek, B., Štadler, C., & Svoboda, J. (2023). Metaverse and AI digital twinning of 42SiCr steel alloys. Mathematics, 11(1), 4.
    View in Google Scholar DOI: https://doi.org/10.3390/math11010004
  48. Koohang, A., Nord, J. H., Ooi, K.-B., Wei-Han Tan, G., Al-Emran, M., Cheng-Xi Aw, E., Baabdullahh, A. M., Buhalis, D., Cham, T.-H., Dennis, C., Dutot, V., Dwivedi, Y. K., Hughes, L., Mogaji, E., Pandey, N., Phau, I., Raman, R., Sharma, A., Sigala, M., Ueno, A., & Wong, L.-W. (2023). Shaping the metaverse into reality: A holistic multidisciplinary understanding of opportunities, challenges, and avenues for future investigation. Journal of Computer Information Systems, 63(3), 735‒765.
    View in Google Scholar DOI: https://doi.org/10.1080/08874417.2023.2165197
  49. Kshetri, N. (2023a). The economics of the industrial metaverse. IT Professional, 25(1), 84‒88.
    View in Google Scholar DOI: https://doi.org/10.1109/MITP.2023.3236494
  50. Kshetri, N. (2023b). Metaverse technologies in product management, branding and communications: Virtual and augmented reality, artificial intelligence, non-fungible tokens and brain‒computer interface. Central European Management Journal, 31(4), 511‒521.
    View in Google Scholar DOI: https://doi.org/10.1108/CEMJ-08-2023-0336
  51. Laviola, E., Gattullo, M., Manghisi, V. M., Fiorentino, M., & Uva, A. E. (2022). Minimal AR: Visual asset optimization for the authoring of augmented reality work instructions in manufacturing. International Journal of Advanced Manufacturing Technology, 119, 1769–1784.
    View in Google Scholar DOI: https://doi.org/10.1007/s00170-021-08449-6
  52. Lăzăroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriță, O. (2022). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047–1080.
    View in Google Scholar DOI: https://doi.org/10.24136/oc.2022.030
  53. Lăzăroiu, G., Bogdan, M., Geamănu, M., Hurloiu, L., Luminița, L., & Ștefănescu, R. (2023). Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management. Oeconomia Copernicana, 14(3), 707–730.
    View in Google Scholar DOI: https://doi.org/10.24136/oc.2023.021
  54. Lee, J., & Kundu, P. (2022). Integrated cyber-physical systems and industrial metaverse for remote manufacturing. Manufacturing Letters, 34, 12‒15.
    View in Google Scholar DOI: https://doi.org/10.1016/j.mfglet.2022.08.012
  55. Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis D., & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279‒295.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jmsy.2022.09.017
  56. Lewandowska, A., Berniak-Woźny, J., & Ahmad, N. (2023). Competitiveness and innovation of small and medium enter-prises under Industry 4.0 and 5.0 challenges: A comprehensive bibliometric analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 1045–1074.
    View in Google Scholar DOI: https://doi.org/10.24136/eq.2023.033
  57. Li, K., Lau, B. P. L., Yuan, X., Ni, W., Guizani, M., & Yuen, C. (2023a). Toward ubiquitous semantic metaverse: Challenges, approaches, and opportunities. IEEE Internet of Things Journal, 10(24), 21855‒21872.
    View in Google Scholar DOI: https://doi.org/10.1109/JIOT.2023.3302159
  58. Li, Q., Kong, L., Min, X., & Zhang, B. (2023b). DareChain: A blockchain-based trusted collaborative network infrastructure for metaverse. International Journal of Crowd Science, 7(4), 168‒179.
    View in Google Scholar DOI: https://doi.org/10.26599/IJCS.2023.9100025
  59. Li, X., Tian, Y., Ye, P., Duan, H., & Wang, F.-Y. (2023c). A novel scenarios engineering methodology for foundation models in metaverse. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2148‒2159.
    View in Google Scholar DOI: https://doi.org/10.1109/TSMC.2022.3228594
  60. Liu, J., Ma, C., & Wang, S. (2023a). Thermal-structure finite element simulation system architecture in a cloud-edge-end collaborative environment. Journal of Intelligent Manufacturing.
    View in Google Scholar DOI: https://doi.org/10.1007/s10845-023-02269-z
  61. Liu, S., Xie, J., & Wang, X. (2023b). QoE enhancement of the industrial metaverse based on mixed reality application optimization. Displays, 79, 102463.
    View in Google Scholar DOI: https://doi.org/10.1016/j.displa.2023.102463
  62. Lyu, Z., & Fridenfalk, M. (2023). Digital twins for building industrial metaverse. Journal of Advanced Research.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jare.2023.11.019
  63. Ma, S., Liu, H., Pan, N., & Wang, S. (2023). Study on an autonomous distribution system for smart parks based on parallel system theory against the background of Industry 5.0. Journal of King Saud University ‒ Computer and Information Sciences, 35(7), 101608.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jksuci.2023.101608
  64. Magalhães, L. C., Magalhães, L. C., Ramos, J. B., Moura, L. R., de Moraes, R. E. N., Gonçalves, J. B., Hisatugu, W. H., Souza, M. T., de Lacalle, L. N. L., & Ferreira, J. C. E. (2022). Conceiving a digital twin for a flexible manufacturing system. Applied Sciences, 12(19), 9864.
    View in Google Scholar DOI: https://doi.org/10.3390/app12199864
  65. Maier, M., Hosseini, N., & Soltanshahi, M. (2024). INTERBEING: On the symbiosis between INTERnet and human BEING. IEEE Consumer Electronics Magazine, 13(3), 98‒106.
    View in Google Scholar DOI: https://doi.org/10.1109/MCE.2023.3319849
  66. Meng, Z., Chen, K., Diao, Y., She, C., Zhao, G., Imran, M. A., & Vucetic, B. (2024). Task-oriented cross-system design for timely and accurate modelling in the metaverse. IEEE Journal on Selected Areas in Communications, 42(3), 752‒766.
    View in Google Scholar DOI: https://doi.org/10.1109/JSAC.2023.3345398
  67. Mosco, V. (2023). Into the metaverse: Technical challenges, social problems, utopian visions, and policy principles. Javnost ‒ The Public, 30(2), 161‒173.
    View in Google Scholar DOI: https://doi.org/10.1080/13183222.2023.2200688
  68. Mourad, N., Alsattar, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Sangaiah, A. K., & Pedrycz, W. (2023). Optimising control engineering tools using digital twin capabilities and other cyber-physical metaverse manufacturing system components. IEEE Transactions on Consumer Electronics.
    View in Google Scholar DOI: https://doi.org/10.1109/TCE.2023.3326047
  69. Mourtzis, D., & Angelopoulos, J. (2023). Development of an extended reality-based collaborative platform for engineering education: Operator 5.0. Electronics, 12(17), 3663.
    View in Google Scholar DOI: https://doi.org/10.3390/electronics12173663
  70. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2023a). Blockchain integration in the era of industrial metaverse. Applied Sciences, 13(3), 1353.
    View in Google Scholar DOI: https://doi.org/10.3390/app13031353
  71. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2023b). The future of the human–machine interface (HMI) in society 5.0. Future Internet, 15(5), 162.
    View in Google Scholar DOI: https://doi.org/10.3390/fi15050162
  72. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2024). Unmanned aerial vehicle (UAV) path planning and control assisted by augmented reality (AR): The case of indoor drones. International Journal of Production Research, 62(9), 3361‒3382.
    View in Google Scholar DOI: https://doi.org/10.1080/00207543.2023.2232470
  73. Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13(3), 1681.
    View in Google Scholar DOI: https://doi.org/10.3390/app13031681
  74. Nair, M. R., Bindu, N., Jose, R., & Satheesh Kumar, K. (2024). From assistive technology to the backbone: The impact of blockchain in manufacturing. Evolutionary Intelligence, 17(3), 1257–1278.
    View in Google Scholar DOI: https://doi.org/10.1007/s12065-023-00872-w
  75. Navarro, J. M., & Pita, A. (2023). Machine learning prediction of the long-term environmental acoustic pattern of a city location using short-term sound pressure level measurements. Applied Sciences, 13(3), 1613.
    View in Google Scholar DOI: https://doi.org/10.3390/app13031613
  76. Negri, E., & Abdel-Aty, T. A. (2023). Clarifying concepts of metaverse, digital twin, digital thread and AAS for CPS-based production systems. IFAC-PapersOnLine, 56(2), 6351‒6357.
    View in Google Scholar DOI: https://doi.org/10.1016/j.ifacol.2023.10.818
  77. Netland, T., Stegmaier, M., Primultini, C., & Maghazei, O. (2023). Interactive mixed reality live streaming technology in manufacturing. Manufacturing Letters, 38, 6‒10.
    View in Google Scholar DOI: https://doi.org/10.1016/j.mfglet.2023.08.141
  78. Ooi, K.-B., Wei-Han Tan, G., Al-Emran, M., Al-Sharafi, M. A., Arpaci, I., Zaidan, A. A., Lee, V.-H., Wong, L.-W., Deveci, M., & Iranmanesh, M. (2023). The metaverse in engineering management: Overview, opportunities, challenges, and future research agenda. IEEE Transactions on Engineering Management.
    View in Google Scholar DOI: https://doi.org/10.1109/TEM.2023.3307562
  79. Özkal, İ., Özkan, İ. A., & Başçiftçi, F. (2024). Metaverse token price forecasting using artificial neural networks (ANNs) and adaptive neural fuzzy inference system (ANFIS). Neural Computing and Applications, 36, 3267–3290.
    View in Google Scholar DOI: https://doi.org/10.1007/s00521-023-09228-y
  80. Park, A., Wilson, M., Robson, K., Demetis, D., & Kietzmann, J. (2023). Interoperability: Our exciting and terrifying Web3 future. Business Horizons, 66(4), 529‒541.
    View in Google Scholar DOI: https://doi.org/10.1016/j.bushor.2022.10.005
  81. Qian, F., Tang, Y., & Yu, X. (2023). The future of process industry: A cyber–physical–social system perspective. IEEE Transactions on Cybernetics.
    View in Google Scholar DOI: https://doi.org/10.1109/TCYB.2023.3298838
  82. Reiman, A., Kaivo-oja, J., Parviainen, E., Takala, E.-P., & Lauraeus, T. (2023). Human work in the shift to Industry 4.0: A road map to the management of technological changes in manufacturing. International Journal of Production Research.
    View in Google Scholar DOI: https://doi.org/10.1080/00207543.2023.2291814
  83. Rejeb, A., Rejeb, K., & Treiblmaier, H. (2023). Mapping metaverse research: Identifying future research areas based on bibliometric and topic modelling techniques. Information, 14(7), 356.
    View in Google Scholar DOI: https://doi.org/10.3390/info14070356
  84. Ritterbusch, G. D., & Teichmann, M. R. (2023). Defining the metaverse: A systematic literature review. IEEE Access, 11, 12368‒12377.
    View in Google Scholar DOI: https://doi.org/10.1109/ACCESS.2023.3241809
  85. Salam, A., Javaid, Q., Ahmad, M., Wahid, I., & Arafat, M. Y. (2023). Cluster-based data aggregation in flying sensor networks enabled Internet of Things. Future Internet, 15(8), 279.
    View in Google Scholar DOI: https://doi.org/10.3390/fi15080279
  86. Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with artificial intelligence (AI)-enabled malware and intrusion detection. Journal of Industrial Information Integration, 36, 100520.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jii.2023.100520
  87. Semeraro, C., Alyousuf, N., Kedir, N. I., & Lail, E. A. (2023). A maturity model for evaluating the impact of Industry 4.0 technologies and principles in SMEs. Manufacturing Letters, 37, 61‒65.
    View in Google Scholar DOI: https://doi.org/10.1016/j.mfglet.2023.07.018
  88. Siriweera, A., & Naruse, K. (2023). QoS-aware federated crosschain-based model-driven reference architecture for IIoT sensor networks in distributed manufacturing. IEEE Sensors Journal, 23(23), 29630‒29644.
    View in Google Scholar DOI: https://doi.org/10.1109/JSEN.2023.3325342
  89. Starly, B., Koprov, P., Bharadwaj, A., Batchelder, T., & Breitenbach, B. (2023). Unreal’ factories: Next generation of digital twins of machines and factories in the industrial metaverse. Manufacturing Letters, 37, 50‒52.
    View in Google Scholar DOI: https://doi.org/10.1016/j.mfglet.2023.07.021
  90. Stary, C. (2023). Digital process twins as intelligent design technology for engineering metaverse/XR applications. Sustainability, 15(22), 16062.
    View in Google Scholar DOI: https://doi.org/10.3390/su152216062
  91. Stavroulakis, G. E., Charalambidi, B. G., & Koutsianitis, P. (2022). Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures. Applied Sciences, 12(23), 11997.
    View in Google Scholar DOI: https://doi.org/10.3390/app122311997
  92. Stodt, F., Stodt, J., & Reich, C. (2023). Blockchain secured dynamic machine learning pipeline for manufacturing. Applied Sciences, 13(2), 782.
    View in Google Scholar DOI: https://doi.org/10.3390/app13020782
  93. Stothard, P. (2023). Mining metaverse – A future collaborative tool for best practice mining. Mining Technology, 132(3), 165‒178.
    View in Google Scholar DOI: https://doi.org/10.1080/25726668.2023.2235155
  94. Striffler, N., & Voigt, T. (2023). Concepts and trends of virtual commissioning – A comprehensive review. Journal of Manufacturing Systems, 71, 664‒680.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jmsy.2023.10.013
  95. Tan, G. W.-H., Aw, E. C.-X., Cham, T.-H., Ooi, K.-B., Dwivedi, Y. K., Alalwan, A. A., Balakrishnan, J., Chan, H. K., Hew, J.-J., Hughes, L., Jain, V., Lee, V. H., Lin, B., Rana, N. P., & Tan, T. M. (2023). Metaverse in marketing and logistics: The state of the art and the path forward. Asia Pacific Journal of Marketing and Logistics, 35(12), 2932‒2946.
    View in Google Scholar DOI: https://doi.org/10.1108/APJML-01-2023-0078
  96. Theodoropoulos, N., Kampourakis, E., Andronas, D., & Makris, S. (2023). Cyber-physical systems in non-rigid assemblies: A methodology for the calibration of deformable object reconstruction models. Journal of Manufacturing Systems, 70, 525‒537.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jmsy.2023.08.022
  97. Tlili, A., Huang, R., & Kinshuk (2023). Metaverse for climbing the ladder toward ‘Industry 5.0’ and ‘Society 5.0’? Service Industries Journal, 43(3/4), 260‒287.
    View in Google Scholar DOI: https://doi.org/10.1080/02642069.2023.2178644
  98. Truong, V. T., Le, L., & Niyato, D. (2023). Blockchain meets metaverse and digital asset management: A comprehensive survey. IEEE Access, 11, 26258‒26288.
    View in Google Scholar DOI: https://doi.org/10.1109/ACCESS.2023.3257029
  99. Wan, X., Zhang, G., Yuan, Y., & Chai, S. (2023). How to drive the participation willingness of supply chain members in metaverse technology adoption? Applied Soft Computing, 145, 110611.
    View in Google Scholar DOI: https://doi.org/10.1016/j.asoc.2023.110611
  100. Wan, Z., Gao, Z., Di Renzo, M., & Hanzo, L. (2022). The road to Industry 4.0 and beyond: A communications-, information-, and operation technology collaboration perspective. IEEE Network, 36(6), 157‒164.
    View in Google Scholar DOI: https://doi.org/10.1109/MNET.008.2100484
  101. Wang, B., Zheng, P., Yin, Y., Shih, A., & Wang, L. (2022b). Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective. Journal of Manufacturing Systems, 63, 471‒490.
    View in Google Scholar DOI: https://doi.org/10.1016/j.jmsy.2022.05.005
  102. Wang, H., Ning, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., Ding, J., & Daneshmand, M. (2023a). A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges. IEEE Internet of Things Journal, 10(16), 14671‒14688.
    View in Google Scholar DOI: https://doi.org/10.1109/JIOT.2023.3278329
  103. Wang, P., Wei, Z., Qi, H., Wan, S., Xiao, Y., Sun, G., & Zhang, Q. (2024). Mitigating poor data quality impact with federated unlearning for human-centric metaverse. IEEE Journal on Selected Areas in Communications, 42(4), 832‒849.
    View in Google Scholar DOI: https://doi.org/10.1109/JSAC.2023.3345388
  104. Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023b). A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects. IEEE Internet of Things Journal, 10(17), 14965‒14987.
    View in Google Scholar DOI: https://doi.org/10.1109/JIOT.2023.3263909
  105. Wang, Y., Tian, Y., Wang, J., Cao, Y., Li, S., & Tian, B. (2022a). Integrated inspection of QoM, QoP, and QoS for AOI industries in metaverses. IEEE/CAA Journal of Automatica Sinica, 9(12), 2071‒2078.
    View in Google Scholar DOI: https://doi.org/10.1109/JAS.2022.106091
  106. Wu, D., Yang, Z., Zhang, P., Wang, R., Yang, B., & Ma, X. (2023). Virtual-reality interpromotion technology for metaverse: A survey. IEEE Internet of Things Journal, 10(18), 15788‒15809.
    View in Google Scholar DOI: https://doi.org/10.1109/JIOT.2023.3265848
  107. Xiang, W., Yu, K., Han, F., Fang, L., He, D., & Han, Q.-L. (2024). Advanced manufacturing in Industry 5.0: A survey of key enabling technologies and future trends. IEEE Transactions on Industrial Informatics, 20(2), 1055‒1068.
    View in Google Scholar DOI: https://doi.org/10.1109/TII.2023.3274224
  108. Xinyi, T., Juuso, A., Riku, A.-L., Chao, Y., Pauli, S., & Kari, T. (2023). TwinXR: method for using digital twin descriptions in industrial eXtended reality applications. Frontiers in Virtual Reality, 4, 1019080.
    View in Google Scholar DOI: https://doi.org/10.3389/frvir.2023.1019080
  109. Xuhong, L., & Xuan, Y. (2023). Green transformational leadership and employee organizational citizenship behavior for the environment in the manufacturing industry: A social information processing perspective. Frontiers in Psychology, 13, 1097655.
    View in Google Scholar DOI: https://doi.org/10.3389/fpsyg.2022.1097655
  110. Yang, J., Wang, X., & Zhao, Y. (2022). Parallel manufacturing for industrial metaverses: A new paradigm in smart manufacturing. IEEE/CAA Journal of Automatica Sinica, 9(12), 2063‒2070.
    View in Google Scholar DOI: https://doi.org/10.1109/JAS.2022.106097
  111. Yi, H., Qu, T., Zhang, K., Li, M., Huang, G. Q., & Chen, Z. (2023). Production logistics in Industry 3.X: Bibliometric analysis, frontier case study, and future directions. Systems, 11(7), 371.
    View in Google Scholar DOI: https://doi.org/10.3390/systems11070371
  112. Ying, K., Gao, Z., Chen, S., Zhou, M., Zheng, D., Chatzinotas, S., Ottersten, B., & Poor, H. V. (2023). Quasi-synchronous random access for massive MIMO-based LEO satellite constellations. IEEE Journal on Selected Areas in Communications, 41(6), 1702‒1722.
    View in Google Scholar DOI: https://doi.org/10.1109/JSAC.2023.3273699
  113. Yu, B., Liu, Y., Ren, S., Zhou, Z., & Liu, J. (2023). METAseen: Analyzing network traffic and privacy policies in Web 3.0 based metaverse. Digital Communications and Networks.
    View in Google Scholar DOI: https://doi.org/10.1016/j.dcan.2023.11.006
  114. Zaidan, A. A., Alsattar, H. A., Qahtan, S., Deveci, M., Pamucar, D., & Hajiaghaei-Keshteli, M. (2023). Uncertainty decision modelling approach for control engineering tools to support industrial cyber-physical metaverse smart manufacturing systems. IEEE Systems Journal, 17(4), 5303‒5314.
    View in Google Scholar DOI: https://doi.org/10.1109/JSYST.2023.3266842
  115. Zeng, S., Li, Z., Yu, H., Zhang, Z., Luo, L., Li, B., & Niyato, D. (2023). HFedMS: Heterogeneous federated learning with memorable data semantics in industrial metaverse. IEEE Transactions on Cloud Computing, 11(3), 3055‒3069.
    View in Google Scholar DOI: https://doi.org/10.1109/TCC.2023.3254587
  116. Zhang, L., Du, Q., Lu, L., & Zhang, S. (2023). Overview of the integration of communications, sensing, computing, and storage as enabling technologies for the metaverse over 6G networks. Electronics, 12(17), 3651.
    View in Google Scholar DOI: https://doi.org/10.3390/electronics12173651
  117. Zhou, X., Liu, C., & Zhao, J. (2023). Resource allocation of federated learning for the metaverse with mobile augmented reality. IEEE Transactions on Wireless Communications.
    View in Google Scholar DOI: https://doi.org/10.1109/ICC45041.2023.10279550

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