Bibliographic record
Abstract
In today's wireless sensor networks there is a need to improve macro programming of the sensor nodes so that sensor nodes can execute various applications in a flexible manner. There is a need to contribute to macro programming of wireless sensor networks by developing new code dissemination techniques which can disseminate the code into designated sensor nodes. We present a new algorithm called multicast deluge (MDeluge) which can be used to disseminate the code image into a designated subnet of a wireless sensor network. MDeluge disseminates code in the sensor network using a tree which is formed when the sensor nodes send code request messages. Micro server keeps the code and sends it based on the requests. MDeluge disseminates binary code as well as capsules of Mate. We extend MDeluge for the sensor networks that are geographically distributed and that have moving nodes. Assuming a grid structured wireless sensor network we present an analysis of various message costs as well as the overall cost of data messages of MDeluge. Simulation of MDeluge shows that MDeluge performs better than Deluge to disseminate the code into designated sensor nodes
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".