Simulation of large wireless sensor networks using Cell-DEVS
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
The advancement of electronic sensing devices, microcomputers and wireless communication devices has lead to creation of new smart sensors, which can monitor actuate, compute and communicate. Typically, these sensors are deployed in non-deterministic mode (randomly) when deployed in large numbers. These sensor devices have the capability to self-organize into the so-called Wireless Sensor Networks (WSN). WSN are ad-hoc networks, consisting of these spatially distributed sensing and processing devices. We introduce a model and a simulation study of these Large Wireless Sensor Networks (WSN) by implementing the Topology Control Algorithm. We use the Cell-DEVS formalism, which enables efficient execution of cellular models. Thereafter, we observe and evaluate the behavior of sensor nodes and entire WSN from the simulation results obtained, under different test scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it