A high performance cluster-based broadcasting algorithm for wireless ad hoc networks based on a novel gateway selection approach
Bibliographic record
Abstract
In this paper we propose a new broadcasting algorithm for a "stability-based clustering algorithm". In this algorithm a novel traffic isolation method has been used to reduce the control message exchanges. 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 ones, 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 the other clusters. In the proposed method we significantly reduce the broadcast overhead and also improve the broadcast delivery ratio in mobile networks. Considering the effect of mobility and node density, a number of simulations have been conducted. The results show that the number of re-broadcasting nodes is less than 25% of the total number of nodes gwhich is almost constant with the node mobility, while the broadcast coverage is close to 100%. Moreover, we study the flooding parameters in light and dense networks and show the improvement of the broadcasting performance in comparison to other methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".