RNG and internal node based broadcasting algorithms for wireless one-to-one networks
Bibliographic record
Abstract
In a multihop wireless network, each node has a transmission radius and is able to send a message to one of its neighbors (one-to-one) or all of its neighbors (one-to-all) that are located within the radius. In a broadcasting task, a source node needs to send the same message to all the nodes in the network. In this paper, we propose to reduce the communication overhead of broadcasting algorithm for one-to-one model by applying the concepts of planar graphs such as RNG (relative neighborhood graphs) and connected dominating sets determined by internal nodes. Regular message exchanges between neighbors, which include location updates or signal strengths, suffice to maintain these structures, and they therefore do not impose additional communication overhead. In internal node based broadcasting, 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. A neighbor elimination scheme is added to the internal node concept, to improve its performance. Similarly, only edges in a planar subgraph may be used for retransmissions. The reduction in communication overhead for broadcasting task, with respect to existing methods, is measured experimentally. The number of retransmissions is reduced to about 50% for sparse networks and to about 5% for dense networks, and the overhead with respect to ideal solution is up to 20% (for 100 nodes).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".