Connectivity Trade-offs in 3D Wireless Sensor Networks Using Directional Antennae
Bibliographic record
Abstract
We consider a 3D antenna orientation problem for maintaining connectivity of a wireless network in 3D space using only directional antennae. Sensors are located at points in 3D space and are equipped with directional antennae. The strong connectivity antenna orientation problem is concerned with deciding whether or not for given solid angle Ω and range r it is possible to orient the antennae so as to ensure that the sensor network resulting from the induced transmissions is strongly connected. In this paper we 1) present an algorithm ensuring optimal antenna range for the case when Ω ≥ 18π/5, 2) show that determining whether or not there exists a strong orientation of directional sensors of solid angle Ω; 0, and 3) provide an algorithm for approximating the antennae range so as to ensure strong connectivity of the resulting graph, provided the solid angle of the antennae is 2π ≤ Ω <; 18π/5· In addition, we study the effect of replacing omnidirectional antennae with directional antennae on the hop stretch factor of the resulting network of directional antennae and present some simulation results on the variation of hop stretch factor with different network sizes and solid angles of directional antennae. This is the first paper concerning the strong connectivity antennae orientation problem in 3D space.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".