Improving broadcast performance by traffic isolation in wireless<i>ad hoc</i>networks
Bibliographic record
Abstract
Abstract In this paper we propose a new broadcasting algorithm. In the proposed method we significantly reduce the broadcast overhead and also improve the broadcast delivery ratio in mobile networks. A novel traffic isolation method has been used which reduces the control message exchange. The proposed broadcasting method is based on a clustering method called ‘stability‐based clustering algorithm’ which had been proposed before. The broadcasting traffic is divided into internal (flow inside a cluster) and external traffic (flow among the clusters). For internal flooding traffic, cluster‐heads and gateways are responsible for re‐broadcasting but for external type, border nodes may perform the forwarding function as well. This simplifies the gateway selection method through the local selection of gateway nodes by its cluster head. Therefore, a cluster head selects gateway in its own cluster without any knowledge of other clusters. Considering the effect of mobility and node density, simulations have been conducted in a number of wireless environments. Simulation results show the broadcast coverage is close to 100% at different node speeds. Moreover, we study the broadcast parameters in light and dense networks and show improvement of the overhead and the number of forward nodes in comparison to other broadcasting methods. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".