Bibliographic record
Abstract
A set of wireless sensor network, composed of a number of sensors and cluster-head nodes and sink nodes, make decisions and implement them for the set of information collected from the environment using wireless communication.In present study, by entering the areas of wireless sensor networks and the GTS allocation, the emergency data GTS (EDG) algorithm, which programmed based on normal and emergency data, is investigated and a solution to the release delay improvement is proposed.The EDG algorithm has disadvantages in the area of emergency data transmission under the critical conditions: it causes an increase in the release delay in the sensor network.Thus, in present study, by implementing an appropriate threshold level and prioritizing the nodes of the first cluster-head, the high traffic of the network under the critical conditions can be controlled and the main release delay of the network can be reduced.Reduction in release delay results in reduction of the loss of emergency data in the network and increase of the network efficiency under the critical conditions.Finally, the comparison graph of the release delay and the main algorithm is investigated and the results are summarized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.918 | 0.921 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".