MobileGrid: capacity-aware topology control in mobile ad hoc networks
Bibliographic record
Abstract
Since wireless mobile ad hoc networks are arbitrarily and dynamically deployed, the network performance may be affected by many unpredictable factors such as the total number of nodes, physical area of deployment, and transmission range on each node. Previous research results only focus on maximizing power efficiency through dynamically adjusting the transmission range on each node. Via extensive performance evaluations, we have observed that the network performance is linked with a single parameter, the network contention index, which each node may estimate in a fully distributed fashion. This paper introduces the definition of such a parameter, which is derived from relevant parameters such as the number of nodes and the transmission range on each node. With the presence of node mobility, we present a detailed study of the effects of contention index on the network performance, with respect to network capacity and power efficiency. We have observed that the capacity is a concave function of the contention index. We further show that the impact of node mobility is minimal on the network performance when the contention index is high. Based on these important observations, we present MobileGrid, a fully distributed topology control algorithm that attempts to achieve the best possible network capacity, by maintaining optimal contention index via dynamically adjusting the transmission range on each of the nodes in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".