Local cooperative relay for opportunistic data forwarding in mobile ad-hoc networks
Bibliographic record
Abstract
Opportunistic data forwarding draws more and more attention in the research community of wireless network after the initial work ExOR was published. However, as far as we know, all existing opportunistic data forwarding only use the nodes which are included in the forwarder list in the entire forwarding progress. In fact, even if a node is not a listed forwarder in the forwarder list, but it is on the direction from source node to destination node, and when it successfully overhears some packets by opportunity, the node actually can be utilized in the opportunistic data forwarding progress. In this paper, we propose the local cooperative relay for opportunistic data forwarding in mobile ad-hoc networks. In general, three contributions we have in this paper, 1) we open more node to participate in the opportunistic data forwarding even though the nodes are not included in the forwarder list, 2) we propose the procedure to select the best local relay node, namely the helper-node, from many candidates but require no inner communication between them, 3) the helper-node is selected just when it is needed, and the such real time selection can tolerate and bridge vulnerable links in mobile networks.
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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 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".