Digital twin-based virtual factory and cyber-physical production systems, collaborative autonomous robotic and networked manufacturing technologies, and enterprise and business intelligence algorithms for industrial metaverse
DOI:
https://doi.org/10.24136/eq.3557Keywords:
digital twin, industrial metaverse, virtual factory, cyber-physical production, networked manufacturing, enterprise and business intelligenceAbstract
Research background: Cognitive computing and robotic technologies, enterprise digital twin system modeling, and sensory perception algorithms optimize industrial big data exchange and production collaboration, production floor management, and smart device 3D simulation and visualization in the Industry 5.0 metaverse and virtual shop floor environments. Enterprise metaverse business operations, multi-granularity cognitive computing, and industrial big data fusion simulation integrate virtual and augmented reality technologies, collaborative robotic and industrial cyber-physical production systems, and artificial intelligence-enabled edge computing and Internet of Everything devices in mobile edge computing environments. Cloud-based production and digital twin Internet of Things networks, 3D immersive virtual reality and realistic 3D scene construction technologies, and cyber-physical production and business process management systems articulate smart production engineering and management, artificial intelligence-driven physics simulation, and Internet of Things-based robotic manufacturing in highly realistic industrial product representations and 3D virtual spaces with regard to big data-driven business decisions.
Purpose of the article: We show that 3D immersive virtual reality and digital twin metaverse technologies, spatial scanning modeling, and autonomous robotic and virtual factory simulation systems are pivotal in immersive 3D process management, industrial manufacturing production value, and knowledge accumulation in synthetic simulated environments. 3D simulation-based industrial processes and immersive experiences can be attained through cognitive computing and robotic technologies, multi-modal information fusion, autonomous intelligence generation, and multiple production process management in immersive 3D metaverse environments. Immersive, multisensory, and augmented digital experiences can be attained through 3D factory simulation and immersive extended reality technologies, cognitive robotic process automation, autonomous robotic and industrial machine learning systems, and task allocation optimization in computer-generated 3D virtual environments.
Methods: We analyzed and synthesized common operations for the first 60 companies in industrial metaverse on ensun (AI-based supplier sourcing tool’s) website in terms of key takeaway, working industry, type of company, and specialized areas, and identified three main topics.
Findings & value added: The main value added derived from our research is that industrial metaverse 3D simulation and modeling, digital twin and remote fault diagnosis technologies, multiphysics simulation and predictive maintenance tools assist industrial big data monitoring and management, Internet of Things-based robotic manufacturing, and multiple processing tasks in 3D digital twin factories. Collaborative autonomous manufacturing operations, artificial intelligence-driven physics simulation, and smart industrial devices and processes necessitate industrial metaverse decentralized federated learning, cognitive computing and robotic technologies, and cognitive digital twins in virtual shop floor environments, generating economic value. 3D simulation and visualization technologies, business intelligence and digital twin-based cyber-physical production systems, and big data-driven forecasting and real-time collision detection algorithms can be harnessed in robotic automation processes, intelligent manufacturing upgrading, and sustainable industrial value creation across 3D digital twin factories and distributed computing environments.
Downloads
References
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. DOI: https://doi.org/10.1108/TG-03-2023-0036
View in Google Scholar
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, 40(20), 7043‒7064 DOI: https://doi.org/10.1080/10447318.2023.2260984
View in Google Scholar
Andronie, M., Lăzăroiu, G., Iatagan, M., Uță, C., Ștefănescu, R., & Cocoșatu, M. (2021). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20), 2497. DOI: https://doi.org/10.3390/electronics10202497
View in Google Scholar
Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I., Ștefănescu, R., Dijmărescu, A., & Dijmărescu, I. (2023a). Big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools in the Internet of Robotic Things. ISPRS International Journal of Geo-Information, 12(2), 35. DOI: https://doi.org/10.3390/ijgi12020035
View in Google Scholar
Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., Dijmărescu, A., & Dijmărescu, I. (2023b). Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the Internet of Robotic Things. Electronics, 12(1), 22. DOI: https://doi.org/10.3390/electronics12010022
View in Google Scholar
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. DOI: https://doi.org/10.26599/TST.2023.9010052
View in Google Scholar
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. DOI: https://doi.org/10.1109/ACCESS.2023.3337806
View in Google Scholar
Bellalouna, F., & Puljiz, D. (2023). Use case for the application of the industrial metaverse approach for engineering design review. Procedia CIRP, 119, 638‒643. DOI: https://doi.org/10.1016/j.procir.2023.03.116
View in Google Scholar
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. DOI: https://doi.org/10.1108/IJEBR-01-2023-0041
View in Google Scholar
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. DOI: https://doi.org/10.1109/MWC.2001.2200396
View in Google Scholar
Chai, Y., Qian, J., & Younas, M. (2023). Metaverse: Concept, key technologies, and vision. International Journal of Crowd Science, 7(4), 149‒157. DOI: https://doi.org/10.26599/IJCS.2023.9100024
View in Google Scholar
Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2022). Examining the impact of adoption of emerging technology and supply chain resilience on firm performance: moderating role of absorptive capacity and leadership support. IEEE Transactions on Engineering Management, 71, 10373‒10386. https://dx.doi.org/10.1109/TEM.2021.3134188. DOI: https://doi.org/10.1109/TEM.2021.3134188
View in Google Scholar
Chatterjee, S., Chaudhuri, R., Gupta, S., Sivarajah, U., & Bag, S. (2023). Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm. Technological Forecasting and Social Change, 196, 122824. DOI: https://doi.org/10.1016/j.techfore.2023.122824
View in Google Scholar
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. DOI: https://doi.org/10.1016/j.jmsy.2023.10.010
View in Google Scholar
Chen, Y., Huang, W., Jiang, X., Zhang, T., Wang, Y., Yan, B., Wang, Z., Chen, Q., Xing, Y., Li, D., & Long, G. (2023b). UbiMeta: A ubiquitous operating system model for metaverse. International Journal of Crowd Science, 7(4), 180‒189. DOI: https://doi.org/10.26599/IJCS.2023.9100028
View in Google Scholar
Chowdhury, M. (2023). 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. DOI: https://doi.org/10.1504/IJAHUC.2023.134763
View in Google Scholar
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. DOI: https://doi.org/10.1016/j.jmsy.2023.07.016
View in Google Scholar
Erman, B., & Martino, C. D. (2023). Generative network performance prediction with network digital twin. IEEE Network, 37(2), 286‒292. DOI: https://doi.org/10.1109/MNET.002.2200515
View in Google Scholar
Ferrari, F., & McKelvey, F. (2023). Hyperproduction: A social theory of deep generative models. Distinktion: Journal of Social Theory, 24(2), 338‒360. DOI: https://doi.org/10.1080/1600910X.2022.2137546
View in Google Scholar
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. DOI: https://doi.org/10.1109/JSEN.2023.3294962
View in Google Scholar
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. DOI: https://doi.org/10.3390/app122412600
View in Google Scholar
Grieves, M. (2023). Digital twin certified: Employing virtual testing of digital twins in manufacturing to ensure quality products. Machines, 11(8), 808. DOI: https://doi.org/10.3390/machines11080808
View in Google Scholar
Han, J., Yang, M., Chen, X., Liu, H., Wang, Y., Li, J., Su, Z., Li, Z., & Ma, X. (2023). ParaDefender: A scenario-driven parallel system for defending metaverses. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2118‒2127. DOI: https://doi.org/10.1109/TSMC.2022.3228928
View in Google Scholar
Hou, J., Chen, G., Li, Z., He, W., Gu, S., Knoll, A., & Jiang, C. (2024a). Hybrid residual multiexpert reinforcement learning for spatial scheduling of high-density parking lots. IEEE Transactions on Cybernetics, 54(5), 2771‒2783. DOI: https://doi.org/10.1109/TCYB.2023.3312647
View in Google Scholar
Hou, X., Wang, J., Jiang, C., Meng, Z., Chen, J., & Ren, Y. (2024b). 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. DOI: https://doi.org/10.1109/JSAC.2023.3345393
View in Google Scholar
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. DOI: https://doi.org/10.1145/3626315
View in Google Scholar
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. DOI: https://doi.org/10.1109/MWC.003.2200616
View in Google Scholar
Jamshidi, M., Dehghaniyan Serej, A., Jamshidi, A., & Moztarzadeh, O. (2023). The meta-metaverse: Ideation and future directions. Future Internet, 15(8), 252. DOI: https://doi.org/10.3390/fi15080252
View in Google Scholar
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. DOI: https://doi.org/10.1109/ACCESS.2023.3326258
View in Google Scholar
Kaarlela, T., Pitkäaho, T., Pieskä, S., Padrão, P., Bobadilla, L., Tikanmäki, M., Haavisto, T., Blanco Bataller, V., Laivuori, N., Luimula, M. (2023). Towards metaverse: Utilizing extended reality and digital twins to control robotic systems. Actuators, 12(6), 219. DOI: https://doi.org/10.3390/act12060219
View in Google Scholar
Kaigom, E. G. (2024). Metarobotics for industry and society: Vision, technologies, and opportunities. IEEE Transactions on Industrial Informatics, 20(4), 5725‒5736. DOI: https://doi.org/10.1109/TII.2023.3337380
View in Google Scholar
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. DOI: https://doi.org/10.3390/math11010004
View in Google Scholar
Kshetri, N. (2023). The economics of the industrial metaverse. IT Professional, 25(1), 84‒88. DOI: https://doi.org/10.1109/MITP.2023.3236494
View in Google Scholar
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. DOI: https://doi.org/10.1007/s00170-021-08449-6
View in Google Scholar
Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ștefănescu, R., & Dijmărescu, I. (2022). Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the Internet of Manufacturing Things. ISPRS International Journal of Geo-Information, 11(5), 277. DOI: https://doi.org/10.3390/ijgi11050277
View in Google Scholar
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. DOI: https://doi.org/10.1016/j.jmsy.2022.09.017
View in Google Scholar
Li, Q., Kong, L., Min, X., & Zhang, B. (2023). DareChain: A blockchain-based trusted collaborative network infrastructure for metaverse. International Journal of Crowd Science, 7(4), 168‒179. DOI: https://doi.org/10.26599/IJCS.2023.9100025
View in Google Scholar
Liu, J., Ma, C., & Wang, S. (2023). Thermal-structure finite element simulation system architecture in a cloud-edge-end collaborative environment. Journal of Intelligent Manufacturing, 36, 1063‒1094. DOI: https://doi.org/10.1007/s10845-023-02269-z
View in Google Scholar
Lyu, Z., & Fridenfalk, M. (2023). Digital twins for building industrial metaverse. Journal of Advanced Research, 66, 31‒38. DOI: https://doi.org/10.1016/j.jare.2023.11.019
View in Google Scholar
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. DOI: https://doi.org/10.3390/app12199864
View in Google Scholar
Maier, M., Hosseini, N., & Soltanshahi, M. (2024). INTERBEING: On the symbiosis between INTERnet and human BEING. IEEE Consumer Electronics Magazine, 13(3), 98‒106. DOI: https://doi.org/10.1109/MCE.2023.3319849
View in Google Scholar
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. DOI: https://doi.org/10.1109/JSAC.2023.3345398
View in Google Scholar
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, 70(1), 3212‒3221. DOI: https://doi.org/10.1109/TCE.2023.3326047
View in Google Scholar
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2023). Blockchain integration in the era of industrial metaverse. Applied Sciences, 13(3), 1353. DOI: https://doi.org/10.3390/app13031353
View in Google Scholar
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. DOI: https://doi.org/10.1080/00207543.2023.2232470
View in Google Scholar
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. DOI: https://doi.org/10.3390/app13031681
View in Google Scholar
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. DOI: https://doi.org/10.1007/s12065-023-00872-w
View in Google Scholar
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. DOI: https://doi.org/10.1016/j.ifacol.2023.10.818
View in Google Scholar
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, 71, 13882‒13889. DOI: https://doi.org/10.1109/TEM.2023.3307562
View in Google Scholar
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. DOI: https://doi.org/10.1007/s00521-023-09228-y
View in Google Scholar
Park, A., Wilson, M., Robson, K., Demetis, D., & Kietzmann, J. (2023). Interoperability: Our exciting and terrifying Web3 future. Business Horizons, 66(4), 529‒541. DOI: https://doi.org/10.1016/j.bushor.2022.10.005
View in Google Scholar
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. DOI: https://doi.org/10.1080/00207543.2023.2291814
View in Google Scholar
Ritterbusch, G. D., & Teichmann, M. R. (2023). Defining the metaverse: A systematic literature review. IEEE Access, 11, 12368‒12377. DOI: https://doi.org/10.1109/ACCESS.2023.3241809
View in Google Scholar
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. DOI: https://doi.org/10.1016/j.jii.2023.100520
View in Google Scholar
Siriweera, A., & Naruse, K. (2023). QoS-aware federated crosschain-based modeldriven reference architecture for IIoT sensor networks in distributed manufacturing. IEEE Sensors Journal, 23(23), 29630‒29644. DOI: https://doi.org/10.1109/JSEN.2023.3325342
View in Google Scholar
Stary, C. (2023). Digital process twins as intelligent design technology for engineering metaverse/XR applications. Sustainability, 15(22), 16062. DOI: https://doi.org/10.3390/su152216062
View in Google Scholar
Stodt, F., Stodt, J., & Reich, C. (2023). Blockchain secured dynamic machine learning pipeline for manufacturing. Applied Sciences, 13(2), 782. DOI: https://doi.org/10.3390/app13020782
View in Google Scholar
Striffler, N., & Voigt, T. (2023). Concepts and trends of virtual commissioning – A comprehensive review. Journal of Manufacturing Systems, 71, 664‒680. DOI: https://doi.org/10.1016/j.jmsy.2023.10.013
View in Google Scholar
Theodoropoulos, N., Kampourakis, E., Andronas, D., & Makris, S. (2023). Cyberphysical systems in non-rigid assemblies: A methodology for the calibration of deformable object reconstruction models. Journal of Manufacturing Systems, 70, 525‒537. DOI: https://doi.org/10.1016/j.jmsy.2023.08.022
View in Google Scholar
Truong, V. T., Le, L., & Niyato, D. (2023). Blockchain meets metaverse and digital asset management: A comprehensive survey. IEEE Access, 11, 26258‒26288. DOI: https://doi.org/10.1109/ACCESS.2023.3257029
View in Google Scholar
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. DOI: https://doi.org/10.1109/MNET.008.2100484
View in Google Scholar
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. DOI: https://doi.org/10.1109/JIOT.2023.3278329
View in Google Scholar
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. DOI: https://doi.org/10.1109/JIOT.2023.3263909
View in Google Scholar
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. DOI: https://doi.org/10.1109/JSAC.2023.3345388
View in Google Scholar
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. DOI: https://doi.org/10.1109/JIOT.2023.3265848
View in Google Scholar
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. DOI: https://doi.org/10.1109/TII.2023.3274224
View in Google Scholar
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. DOI: https://doi.org/10.3389/frvir.2023.1019080
View in Google Scholar
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. DOI: https://doi.org/10.1109/JAS.2022.106097
View in Google Scholar
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. DOI: https://doi.org/10.1109/JSAC.2023.3273699
View in Google Scholar
Zaidan, A. A., Alsattar, H. A., Qahtan, S., Deveci, M., Pamucar, D., & HajiaghaeiKeshteli, 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. DOI: https://doi.org/10.1109/JSYST.2023.3266842
View in Google Scholar
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. DOI: https://doi.org/10.3390/electronics12173651
View in Google Scholar
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Equilibrium. Quarterly Journal of Economics and Economic Policy
This work is licensed under a Creative Commons Attribution 4.0 International License.
