Application of the DEVS and Cell-DEVS formalisms for modeling networking applications
Bibliographic record
Abstract
We present the use of DEVS and Cell-DEVS formalisms to model different approaches in networking applications. We discuss various applications of discrete event system specifications for modeling and simulation of Wireless networks and Wireless Sensor Networks (WSN). We discuss how to use the Cell-DEVS formalism to model a WSN for investigating on stochastic properties of malware propagation and the intrinsic characteristic of WSN. We also discuss the use of DEVS to model a cellular network including a wide geographical area, various Cells and varied User Equipment. Finally, we discuss how to use the cell DEVS formalism to model mobile networks, and how the Cell-DEVS formalism can be used to track mobile user movement in a covered area. The latter model tries to find out the number of Base Stations which cover a mobile user in different location of an area and how to improve QoS based on different configurations (in particular for the UEs near the cell borders).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".