An IoT Traffic Modeling Framework and its Application to Autonomous Edge Scaling
Bibliographic record
Abstract
Future wireless networks will exhibit heterogeneity of traffic generating sources originated by numerous Internet of Things (IoT) nodes as well as traditional mobile phones. Moreover, the space of novel IoT services is expanding the simple monitoring tasks of IoT nodes to more complex services in which a node can be in a monitoring state and transition autonomously to an alarm state when predefined conditions are detected. The complexity of the envisioned future wireless networks is indeed new to the community with challenges affecting many aspects such as protocol design and network operation mechanisms. Traffic modeling lies at the core of these issues. As the advancement of technologies continues, faithful performance evaluation measures are dependent on the underlying traffic model. In this scope, we propose a Tiered Markov Modulated Poisson Process (TMMPP) that is capable of capturing IoT traffic characteristics, e.g. patterns and seasonality, which occur in long time spans, e.g days, with the flexibility of modeling different IoT service behaviors. Moreover, we study an autonomous edge scaling mechanism as a use case illustrating the benefits of the proposed TMMPP traffic model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".