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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".