MobileGrid: capacity-aware topology control in mobile ad hoc networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it