IoT-driven digital twin systems in sustainable urban development: Analysing barriers using interval-valued Fermatean fuzzy MCDM

Integration of IoT-based digital twin systems is increasingly regarded as one of the most important enablers for advancements in sustainable urban development, with particular focus on smart buildings in developing countries. However, there are many challenges that may be faced in the successful implementation of such systems, considering the numerous impediments related to a lack of technological infrastructure and expertise. The main objective of this research is to analyze the main barriers to integrating IoT data into digital twin systems in smart buildings in developing countries. The major focus is to figure out the key hurdles that should be dealt with in order to achieve proper data integration and realize the full potential of Digital Twin technology. To assess these barriers, the research employs an extended methodology combining the extended Best-Worst Method (BWM) and the Evaluation based on Distance from Aver-age Solution (EDAS) under Interval-Valued Fermatean Fuzzy Sets (IVFFS). This integrated approach-IVFFS-BWM and IVFFS-EDAS strength in evaluating the identified obstacles, having inherent uncertainty and complexities in a decision-making process. The main findings from the numerical results are that the top five most significant integration barriers of IoT data in digital twin systems of smart buildings are: Technical and Computational Issues, which scored 1.948841687 and hence was considered the major challenge; followed by Data Security and Privacy Concerns, scoring 1.660739033; Inadequate Infrastructure with a score of 1.600540681.