RNG and internal node based broadcasting algorithms for wireless one-to-one networks
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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