Mobility-based opportunistic routing for mobile ad-hoc networks
Bibliographic record
Abstract
Opportunistic Routing (OR) is an effective and enhanced routing scheme for wireless multihop environment. OR is an approach which selects a certain number of best forwarders (candidates) at each hop by taking the advantage of the broadcast nature of the wireless medium to reach the destination. When a set of candidates receive the packet, they coordinate with each other to figure out which one has to forward the packet toward the destination. Most of the researches in this area have been done in mesh networks where nodes do not have mobility. In this paper, we propose a new OR protocol for mobile ad hoc scenarios called as Enhanced Mobility-based Opportunistic Routing (EMOR) protocol. To deal with the node mobility, we have proposed a new metric which considers the geographical position of the candidates, the link delivery probability to reach them, the number of the neighboring nodes of the candidate, and the predicted position of nodes using the motion vector of the nodes. We have compared EMOR with five other well-known routing protocols in terms of delivery ratio, end-to-end delay, and expected number of transmissions from source to the destination. Our simulation results show that proposed protocol improves delivery ratio and number of expected transmission in terms of different type of mobility models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".