Internet of Things (IoT) and smart building technologies: A bibliometric analysis from the perspective of energy efficiency and sustainability

Doğukan Karataş; Özlem Uçar

doi:10.35208/ert.1773135

Authors

Doğukan Karataş Trakya University, Institute of Natural and Applied Sciences https://orcid.org/0000-0003-3529-8663
Özlem Uçar Trakya University, Computer Engineering Dept. https://orcid.org/0000-0002-9676-9548

DOI:

https://doi.org/10.35208/ert.1773135

Keywords:

Artificial intelligence, building automation, digitalization, energy management, intelligent buildings, bibliometric analysis

Abstract

Recent urban population growth and environmental degradation have made the integration of digital technologies into the construction sector inevitable, particularly for energy efficiency and sustainability. In this regard, IoT-enabled smart building systems offer innovative solutions to enhance building performance and promote sustainable urban development. This study explores how IoT-integrated smart building systems have evolved in scholarly literature, focusing on energy-efficient and sustainable practices while analyzing structural, thematic, and collaborative dynamics. The aim is to provide a comprehensive bibliometric analysis of the intersection between IoT and smart building technologies based on publications from 2012 to 2025, with the data for 2025 being partial and covering only the first four months of the year. To this end, 233 relevant documents retrieved from the Web of Science and Scopus databases were analyzed using the Bibliometrix package in R Studio, incorporating diverse bibliometric indicators.

Findings reveal a steady increase in academic output, though recent publications exhibit relatively low citation impact. China and Spain lead in publication volume, whereas the United Kingdom and Pakistan distinguish themselves through extensive international collaboration. Keyword and thematic analyses reveal a strong emphasis on digitalization, energy management, user experience, and the integration of artificial intelligence technologies. These results suggest a paradigmatic shift from traditional automation-based frameworks toward cognitive systems capable of learning from environmental data and making autonomous decisions.

Overall, the study highlights the increasing maturity of the research field and underscores its interdisciplinary structure and innovation potential. Future research is recommended to focus on enhancing research capacity in underrepresented regions and on the integration of human-centered design with AI-driven energy optimization systems.

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