ABSTRACT. This paper provides a systematic literature review of studies investigating industrial big data clustering and coordination across interconnected virtual worlds. Throughout August 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “metaverse decentralized governance in integrated industrial Internet of Things autonomous robotic and virtual machine environments” + “digital twin 6G and wireless visual sensor networks,” “deep reinforcement learning and environment mapping algorithms,” and “spatial computing and extended reality technologies.” As research published in 2022 and 2023 was inspected, only 161 articles satisfied the eligibility criteria, and 21 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: BIBOT, Colandr, Eppi-Reviewer, ROBIS, SluRp, and SWIFT-Active Screener.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: digital twin; environment mapping; spatial computing; metaverse decentralized governance; integrated industrial Internet of Things; autonomous robotic and virtual machine environments

How to cite: Zvarikova, K., Ciurlău, C. F., Crîșmariu, O.-D., Gura, K., and Drugău Constantin, A.-L. (2024). “Digital Twin 6G and Wireless Visual Sensor Networks, Deep Reinforcement Learning and Environment Mapping Algorithms, and Spatial Computing and Extended Reality Technologies for Metaverse Decentralized Governance in Integrated Industrial Internet of Things Autonomous Robotic and Virtual Machine Environments,” Journal of Self-Governance and Management Economics 12(4): 24–38. doi: 10.22381/jsme12420242.

Received 12 September 2024 • Received in revised form 23 December 2024
Accepted 25 December 2024 • Available online 29 December 2024