EasiSim: A Scalable Simulator for Wireless Sensor Networks
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
Traditional simulators have deficiencies of scalability, thus they are not so efficient in running simulations with large-scale networks. In this paper, we present a new simulator, namely EasiSim, specifically for evalu-ating sensor net-works on a large scale. EasiSim is featured by organizing its core components, including nodes, topology and scenario, in a hierarchically structured approach. The hierarchically structured organiza-tion enables nodes to process all the concurrent events in one batch, hence reducing the running time by an order of magnitude. Moreover, we propose a visualization scheme based on a Client/Server model which separates the graphical user interface (GUI) from the simulation engine, and therefore the scalability of the simulator will not be decreased by complex GUI. Extensive simulations show that EasiSim outperforms ns-2 in terms of real running time and memory usage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it