Generic 3-D Routing Protocols in Sensing-Covering Regions
Bibliographic record
Abstract
In recent years, sensor networks have been proposed to improve the detection level of natural disasters (e.g. volcanoes, tornadoes, tsunamis). However, this technology has several issues that need to be improved. We, therefore in this paper, focus on two main issues: coverage and routing. For coverage problem, we introduce a new approach for obtaining a static covered network in 3-D environment. This technique is referred to as the chipset coverage model. This would be accomplished by using a small number of sensor nodes in order to save up some energy. For routing issue, we propose several new position-based routing protocols which are the 3-D sensing spheres close to the line routing algorithm (3-D SSL), the 3-D smallest angle to the line routing algorithm (3-D SAL), and the 3-D SSL:SAL routing protocols. We show that the 3-D SAL and the 3-D SSL:SAL routing protocols guarantee the delivery of packets. In our simulation, we show that the 3-D SSL:SAL protocol has similar performance in terms of network (hop) dilation and routing delay to these for an existing 3-D progress-based routing protocol. Moreover, the 3-D SSL:SAL and the 3-D SAL routing protocols outperform an existing 3-D progress-based routing protocol in terms of Euclidean dilation. Thus, the new protocols reduce the energy consumption of the nodes and, therefore, prolong the life of the network.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".