ABSTRACT. The objective of this paper is to systematically review machine and deep learning technologies, wireless sensor networks, and virtual simulation algorithms in digital twin cities. The findings and analyses highlight that smart city governance integrates virtual twin modeling, observational and simulation data, and virtual and augmented reality devices. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin cities” + “machine and deep learning technologies,” “wireless sensor networks,” and “virtual simulation algorithms.” As research published in 2022 was inspected, only 179 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 31 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: virtual; simulation; algorithm; digital; twin; city

How to cite: Balica, R.-Ș. (2022). “Machine and Deep Learning Technologies, Wireless Sensor Networks, and Virtual Simulation Algorithms in Digital Twin Cities,” Geopolitics, History, and International Relations 14(1): 59–74. doi: 10.22381/GHIR14120224.

Received 27 April 2022 • Received in revised form 23 June 2022
Accepted 25 June 2022 • Available online 30 June 2022

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