Multi-retransmission Route Discovery Schemes for Ad Hoc Wireless Network with a Realistic Physical Layer
Bibliographic record
Abstract
During the route discovery process, each node receiving the route request packet (RReq) will retransmit it exactly once. A distant neighbor may accidentally receive/loose the only RReq and use it to announce a new route, although that link is inferior/superior for route reply packets (RRep) or actual message routing. Overall, the constructed route may be far from the optimal. All existing route discovery schemes (including DSR/AODV) apply retransmission during route discovery exactly once (1R). Based on a realistic physical layer model, we propose two new route discovery schemes: n-retransmission (nR, retransmitting exactly n times) and n-retransmission c-reception (ncRR), retransmitting until we either reach a total of n own retransmissions or c copies from neighbors are heard. We compare our two new scheme with the traditional one, under otherwise identical conditions (same metric, same packet reception probability on each link) and the same choices about possibly retransmitting again upon discovering a better route (R+) or discarding it (R1), generating route reply packet for every received RRep (B*), or for first and better discovered routes only (B2), and retransmitting RRep exactly once (A1), up to a maximum of three times (A3), or optimally u times decided by link quality (Au). Experimental results show that the proposed ncRR scheme (for n=2 and c=3 or c=4) achieves the best tradeoff between quality of route, success rate and message overhead in the route discovery process, followed by the nR scheme, and both of them are superior to the existing traditional schemes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".