An efficient heuristic candidate selection algorithm for Opportunistic Routing in wireless multihop networks
Bibliographic record
Abstract
Opportunistic Routing (OR) is a new class of routing protocols that selects the next-hop forwarder on-the-fly. It takes advantage of the broadcast nature of the wireless medium. By selecting a set of candidates for the purpose of forwarding the packet toward the destination, OR improves the reliability of wireless transmissions. In this paper, we propose a new heuristic and a quick candidate selection algorithm based on the link delivery probability from one node to a candidate node. We shall refer to it as Heuristic Candidate selection algorithm based on Optimum delivery Probability (HU-COP). HU-COP finds candidates through links that offer delivery probabilities that are close to optimum. We compare HU-COP with the two other well-known candidate selection algorithms proposed in the literature. The numerical results in terms of the expected number of transmissions show that HU-COP outperforms the well-known ExOR. In addition, the performance of HU-COP is very close to the results of the most efficient algorithm in various scenarios. Furthermore, HU-COP identifies the sets of candidates much faster than the other algorithms under study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".