ABSTRACT. This article reviews and advances existing literature concerning digital twin modeling, multi-sensor fusion technology, and data mining algorithms in cloud and edge computing-based smart city environments. In this research, previous findings were cumulated showing that virtual and augmented reality technologies, machine learning techniques, and data visualization tools are pivotal for urban processes and systems, and we contribute to the literature by indicating that smart city sensing technologies, computer vision algorithms, and mobile social networks articulate immersive virtual worlds. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “cloud and edge computing-based smart city environments” + “digital twin modeling,” “multi-sensor fusion technology,” and “data mining algorithms.” As research published in 2022 was inspected, only 177 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, we selected 32 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR.

Keywords: digital twin modeling; smart city; sensor data; cloud and edge computing

How to cite: Durana, P., Krastev, V., and Buckner, K. (2022). “Digital Twin Modeling, Multi-Sensor Fusion Technology, and Data Mining Algorithms in Cloud and Edge Computing-based Smart City Environments,” Geopolitics, History, and International Relations 14(1): 91–106. doi: 10.22381/GHIR14120226.

Received 22 April 2022 • Received in revised form 23 June 2022
Accepted 25 June 2022 • Available online 30 June 2022