Bibliographic record
Abstract
In a multi-hop wireless network, each node has a transmission radius and is able to send a message to one or all of its neighbors that are located within the radius. In a broadcasting task, a source node sends the same message to all the nodes in the network. Some existing solutions apply re-broadcasting from each cluster-head or border node in a clustered structure. We propose to reduce the communication overhead of the broadcasting algorithm by applying the concept of internal nodes. The maintenance of internal nodes requires much less communication overhead than the maintenance of the cluster structure of the nodes. In one-to-all broadcasting, only the internal nodes forward the message, while in the one-to-one case, messages are forwarded on the edges that connect two internal nodes and on edges that connect each non-internal node with its closest internal node. Existing notions of internal nodes are improved by using node degrees instead of their IDs in internal node decisions. Highest node degrees are also proposed for reducing the number of cluster-heads and border nodes in a clustering algorithm. Further savings are obtained if GPS and the concept of planar subgraphs are used for one-to-one networks. In case of one-to-all model, no re-broadcasting is needed if all neighbors have already received the message. The important features of the proposed algorithms are their reliability, significant savings in the re-broadcasting, and their localized and parameterless behavior. The reduction in the communication overhead for the broadcasting task, with respect to existing methods, is measured experimentally.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".