Babylonian Journal of Internet of Things

Decision Making and IoT: Bibliometric Analysis for Scopus Database

Ruchi Doshi

Department of Computer Science and Engineering, Universidad Azteca, Chalco, Mexico

Kamal Kant Hiran

School of Computer Science & IT, Symbiosis University of Applied Sciences, Indore, India

DOI: https://doi.org/10.58496/BJIoT/2023/003

Keywords: Internet of Things, Decision support systems, Machine learning, Real-time analytics

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Abstract

The Internet of Things (IoT) continues to proliferate through an increasingly connected world, with more smart devices generating vast amounts of data with significant potential to enable intelligent automated decision making across application domains. This bibliometric study comprehensively reviews global literature related to IoT technologies for supporting data-driven decision making to understand key trends in research activities, influential publications and contributors, productive affiliations, and opportunities for additional exploration. Literature data was systematically collected from digital databases like Scopus and Web of Science and statistically analyzed using tools such as VOSviewer to extract insights. The findings reveal exponentially growing publications on IoT-enabled decision making over the past decade, with leading contributors spread across Chinese, European, and North American institutions. There is high collaboration centered around prominent authors as evidenced through co-citation analyses. The highest volume of studies focus specifically on integrating machine learning alongside IoT systems development to enhance automated, real-time analytics for improved situational awareness and responsive, optimized decision making. However, there remain open challenges identified in applying IoT and decision making intersection for fields like business, education, and disaster response. Cluster analysis of frequently employed keywords demonstrates critical areas involving security, efficiency, and specialized decision modelling that warrant deeper investigation. This study serves both as a reference benchmark to represent the current state of IoT and decision making integration across scattered literature as well as inform high potential directions for researchers when shaping future IoT analytics frameworks, distributed decision protocols, and smart environments for automated decision support.