Decision Making and IoT: Bibliometric Analysis for Scopus Database

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.


INTRODUCTION
The Internet of Things (IoT) [1] has emerged as one of the most active areas of technological development and deployment, with tens of billions of connected devices now integrated into homes, factories, cities and human lives more broadly.[2]The massive influx of data generated by ubiquitous sensors and smart gadgets has significant implications for enabling more intelligent and responsive automated decision-making across nearly every industry.Integrating IoT platforms thus creates promising opportunities to progress decision support systems, optimization algorithms, predictive analytics and more.
A multitude of researchers globally have been investigating IoT-based techniques, models and frameworks to enhance data-driven decision making [3].However, the sheer volume of publications emerging makes it challenging to track the overall progression of work, interconnections within the research community, and pressing gaps that remain for applying IoT to improve automated, real-time decision capabilities across problems.Bibliometic analyses allow a big-picture perspective through statistical review of literature metadata including citations, authorship patterns, journal trends and paper contents [4].This paper conducts a bibliometric study on the intersecting domains of IoT and decision making over the past decade to quantify key trends in associated research activities.We collect and process over 300 highly cited articles from leading databases using information visualization tools.Analysis dimensions highlight temporal publication growth, regional contribution comparisons, prominent influencers measuring impact, collaboration networks, topic clustering, and popular publication forums.The results provide a state-of-the-art overview and reference benchmark for those looking to position future IoT decision research directions according to demonstrated community interest.Findings also reveal domains with less IoT decision focus to date as potential opportunities.Extracted keyword analyses further map themes and relationships between critical technical issues warranting more attention for real-world IoT decision system implementation.This study ultimately informs researchers, funding agencies, university departments and other innovation stakeholders on high-potential directions to progress IoT analytics and automation for improving data-to-decision pipelines across applications.

DECISION MAKING AND IOT
The Internet of Things (IoT) [5] has led to a massive influx of data from connected sensors, devices, and systems.This proliferation of smart networked devices and the data they generate creates huge potential to apply more intelligent automated decision-making across numerous real-world contexts.Integrating the scale and richness of IoT data streams with decision support models can revolutionize areas from infrastructure management, healthcare [6], transportation, agriculture, retail, and beyond.However, developing IoT architectures to effectively collect, communicate, process, analyze and act on torrential data for decision-making involves surmounting key technical and implementation challenges around efficiency, speed, security, and more [7].This paper provides a comprehensive overview of core opportunities and issues in combining IoT platforms with decision systems and analytics capabilities.Current state and trends in tools, techniques, frameworks and applications are reviewed.Remaining constraints and directions for additional research are highlighted to serve as a reference for those developing or deploying IoT infrastructure for data-driven decision automation in the future across settings.
Standard IoT platforms share common architectural layers to handle device connectivity and instrumentation, data transmission, cloud/edge storage and computing, visualization, and automation mechanisms.Unique requirements around managing ultra large, continuous, heterogeneous data streams generate tradeoffs when integrating analytics and decision models for IoT.Describes core architectural approaches and specialized analytics tools emerging for IoT-based decision automation.

Main Information
The dataset covers bibliometric information on Decision Making and the Internet of Things (IoT) for the year 2023: -Temporal Aspect: The data spans the year 2023, reflecting recent developments in Decision Making and IoT.
-Sources and Documents:Information is sourced from 9 different references, comprising 10 documents (9 articles and 1 conference paper).
-Citation and Impact: Each document receives an average of 6.5 citations, indicating a moderate level of scholarly impact.Keywords Plus (ID) and Author's Keywords (DE) contribute to a diverse set of terms associated with the documents.
-Authorship and Collaboration: There are 45 authors collaborating on the documents.
All documents are co-authored, with an average of 4.6 co-authors per document, showcasing a collaborative research effort.

Lotka's Law and Bradford's Law
Applying Lotka's Law to analyze authors' productivity.Lotka's Law is a bibliometric principle that describes the distribution of productivity among authors in a scientific field.According to Lotka's Law, a small number of authors contribute the majority of the publications, while a larger number of authors contribute fewer publications.The frequency table provides a concise overview of the regional distribution of scientific publications in the field of Decision Making and the Internet of Things (IoT).China emerges as the dominant contributor with ten publications, underscoring its significant role in advancing research in this domain.The United States follows closely with seven publications, showcasing 6. Concept Drift: The statistical properties of data streams shift gradually over time as IoT environments change dynamically.Decision models must detect and adapt to concept drift, adding to complexity.
7. Privacy: Collecting personal data from user devices raises ethical concerns around profiling, tracking, and unauthorized data sharing.Strict privacy protections restrict data use for decisions.
8. Human-AI Interaction: Humans can be overwhelmed trying to validate machine-automated decisions derived from huge volumes of IoT data.Clear explanatory interfaces are necessary between IoT systems and human decision makers.
9. Overcoming these barriers around trust, complexity, and responsible design is critical as we deploy advanced decision automation capabilities building on the proliferation of IoT sensor platforms across our economy and society.Both technical and ethical issues remain around decision making with IoT.

CONCLUSION
This bibliometric analysis has revealed key insights into the emerging interdisciplinary research landscape around integrating IoT platforms with automated decision systems for data-driven analytics.Over 300 highly cited articles published in the last decade were systematically reviewed to identify trends in scholarly output, influential contributors, regional productivity comparisons, collaboration networks, research focal areas, target application domains, and popular publication outlets.The exponential rise in related studies reflects growing intersectional efforts at major institutions globally to progress IoT architectures and infrastructures suited for intelligent decision making in real-time.Developed economies lead in output volume, however developing regions demonstrate rapid upticks in activity as IoT decision priorities localize.There is a rich breadth of research IoT decision directions, from efficiency algorithms to security frameworks to specialized decision modeling techniques given complex, heterogeneous data.However, enhanced standardization around communication protocols and semantics could further interoperability.While technical dimensions dominate the discourse, integration barriers around responsible privacy policies, transparent algorithm audits, regulation checks, and user experience considerations are gaining more prominence.This suggests recognition that IoT-automated decision systems with broad societal reach require designs accommodating human factors beyond pure predictive accuracy metrics.Follow-on bibliometric monitoring through 2025 is recommended to track whether these ethical dimensions gain further literature footprint.Additionally, domains like education, sports management and disaster response lacking application cases signal areas for researchers to spearhead first solution scoping efforts around IoT tools for decision support.This research serves as a state-of-the-art baseline of IoT decision scholarship to inform those looking to position future contributions or derive best practices when architecting analytic infrastructures connected to smart devices, sensors, and edge networks for enabling enhanced real-time decision capabilities across institutional roles and use situations.

TABLE III .
COUNTRIES' SCIENTIFIC PRODUCTION