IoT Time-Series Traffic Data: Smart City, eHealth, and Smart Factory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This dataset provides synthetic but realistic Internet of Things (IoT) traffic time-series data generated using the novel Tiered Markov-Modulated Stochastic Process (TMMSP) framework. The dataset captures the unique temporal dynamics and stochastic characteristics of three distinct IoT applications: smart city, eHealth, and smart factory systems. Each application's traffic pattern reflects real-world behaviors including human-machine correlation (HMC), sudden data bursts, and application-specific seasonality patterns.The traffic data is presented as time-series with 1-minute resolution over multiple days, incorporating:Daily traffic volume fluctuations reflecting human activity patternsApplication-specific coordinated transmission phases resulting in data burstsVarying traffic intensities based on application characteristicsTemporal correlation between IoT nodesRealistic traffic behavior validated against real IoT application tracesThis dataset is particularly valuable for:Evaluating resource allocation algorithms for edge/cloud computingTesting traffic prediction modelsAnalyzing application-specific IoT network behaviorsDeveloping and validating network slicing strategiesStudying autonomous resource scaling mechanismsThe dataset has been validated through comparison with real IoT traffic patterns and demonstrated utility in evaluating autonomous edge slicing (AES) mechanisms. The included traffic patterns exhibit different human-machine correlations and burst frequencies that match expected behaviors of real-world IoT deployments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.140 |
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 it