On the number of candidates in opportunistic routing for multi-hop wireless networks
Bibliographic record
Abstract
Opportunistic Routing (OR) is a new paradigm that has been investigated as a new way to improve the performance of multihop wireless networks by exploiting the broadcast nature of the wireless medium. In contrast to traditional routing, in OR an ordered set of nodes is selected as potential next-hop forwarders (candidates). Using more number of candidates in OR decreases the number of transmissions in the network, but this comes at the cost of increasing the signaling overhead and also the possibility of having duplicated transmissions which in turn reduces the performance of the OR protocol. The number of candidates that each node can select is an issue which is not well investigated in the literature. In this paper, we propose a Distance-based MAximum number of Candidate Estimation (D-MACE) as an approach to find the number of candidates in each node. In contrast to the traditional approaches in OR which consider an identical number of candidates for all nodes, D-MACE reduces the number of candidates in each node according to the distance between the node and the destination. We evaluate the performance of our proposal, using two relevant candidate selection algorithms. Our results show that D-MACE reduces the number of selected candidates effectively in the network, which improves the network performance compared to the case with the same number of candidates in all nodes.
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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 | 0.000 | 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".