Improved multicriteria spanners for Ad-Hoc networks under energy and distance metrics
Bibliographic record
Abstract
We study the problem of spanner construction in wireless ad-hoc networks through power assignments under two spanner models—distance and energy. In particular, we are interested in asymmetric power assignments so that the induced communication graph holds good distance and energy stretch factors simultaneously. In addition, we consider the following optimization objectives: low total energy consumption, low interference level, low hopdiameter, and high network lifetime. Two node deployment scenarios are studied: random and deterministic. For n random nodes distributed uniformly and independently in a unit square, we present several power assignments with varying construction-time complexities. The results are based on various geometric properties of random points and shortest path tree constructions. Due to the probabilistic nature of this scenario, the probability of our results converges to one as the number of network nodes, n , increases. For the deterministic case, we present two power assignments with nontrivial bounds. These are established in addition to shortcut edges that satisfy desired threshold stretch. To the best of our knowledge, these are the first results for spanner construction in wireless ad-hoc networks with provable bounds for both energy and distance metrics simultaneously. Our power assignments, in addition, try optimizing additional network properties, such as network lifetime, interference, and hop diameter.
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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 | 0.003 | 0.008 |
| 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".