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COLLNET Journal of Scientometrics and Information Management cover

COLLNET Journal of Scientometrics and Information Management

(Since 2007)
Hybrid ·Peer-reviewed·ISSN (Online): 2168-930X·ISSN (Print): 0973-7766

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Half-Yearly Journal: Publishes research and articles on scientometrics and information management, including bibliometric analysis and quality assurance models.

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COLLNET Journal of Scientometrics and Information Management
Open Access Research Article

Trends in IoT applications in smart campuses : A topic modeling approach

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pp. 21–40Vol. 19Issue 1June 2025DOI: 10.47974/CJSIM-2024-017XML
Published Online:
01 Jul 2025
Article type:
Research Article
Language:
EN
Article no.:
CJSIM-2024-017
Pages:
21–40

Abstract

This article provides an in-depth examination of the emerging field of smart campuses and IoT by investigating the thematic landscape of existing literature. The study employs Latent Dirichlet Allocation (LDA) topic modeling to analyze the thematic landscape, identifying key research areas and themes. The dataset consists of 507 research articles retrieved from the Scopus database, covering the period from 2014 to 2023. The LDA model was optimized with α = 0.1 and β = 0.01, and the number of topics (K) was determined as 8 based on the c_v coherence score (0.393), ensuring robust topic extraction. This analysis offers a comprehensive overview of the current state of smart campuses and IoT research, revealing diverse research themes such as “Comprehensive Technology Integration and Management,” “Artificial Intelligence and Cloud-Based Systems,” “Educational Technologies and Innovative Applications,” “Low Power Networks,” “Smart Education Environments,” “Safe and Flexible Smart Campus Architecture,” “Energy Efficient Smart Buildings,” and “Sustainable Smart Education and Urban Development.” These themes highlight the complexity and multidimensionality of the research in this field. While the study provides valuable insights, it is important to acknowledge certain limitations, such as potential biases in dataset selection and the interpretative nature of topic modeling. Moreover, the LDA model’s dependence on predefined topic numbers might limit its ability to capture emerging research themes comprehensively. Future research could refine these findings by incorporating additional datasets, employing alternative modeling approaches such as Hierarchical Dirichlet Process (HDP), and integrating hybrid methodologies to enhance thematic robustness. The study’s findings underscore the diversity and richness of research themes, offering valuable insights for researchers and practitioners, and facilitating more targeted and effective future research and innovation.

Keywords

Smart campusInternet of thingsTopic modelingTrend analysis

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