Analyzing Bibliometric Trends in the Social Internet of Things: A Review and Future Perspectives
Bibliographic record
Abstract
The study aims to fill the gap in the bibliometric analysis of the Social Internet of Things (SIoT) discourse, focusing on recurring patterns, exploring uncharted study domains, and proposing future directions in the developing interdisciplinary realm.The analysis used quantitative data from the Scopus database from 2012 to 2023, covering architecture, trust management, service composition, network navigability, and integration with upcoming technologies.The study revealed a 29.14% yearly expansion rate in SIoT research, demonstrating a dynamic and cooperative research environment.Keyword clusters included social interactions, multidisciplinary perspectives, network architecture, cybersecurity, trust administration, and social networking integration.The bibliometric study provides valuable insights for researchers, practitioners, and policymakers, enabling them to navigate the ever-changing Smart Internet of Things landscape and promote interdisciplinary collaboration for further advancements.Future studies should include qualitative assessments, examine societal implications, and explore future technologies within the Internet of Things (IoT) framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.045 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".