Edge node based greedy routing algorithm (ENBGCR)for cooperative wireless ad hoc networks
Bibliographic record
Abstract
Cooperative diversity techniques have recently received a lot of attention due to their ability to provide spatial diversity in fading wireless environment. It increases link reliability, provides higher capacity, reduces transmit power, and extends transmission range for the same level of performance and modulation rate. This paper studies how to select a path from source to destination that minimizes the end-to-end latency in wireless ad hoc networks by using cooperative transmission at the physical layer. We propose an Edge Node based Greedy Cooperative Routing (ENBGCR) algorithm, where we modify the Geographic Routing (GR) algorithm to incorporate the cooperative transmission and extend the coverage range of the nodes. The numerical analysis are presented to validate the performance of the proposed routing protocol. We found that the ENBGCR algorithm is energy efficient, results in lower end-to-end delay, and involves less number of hops or transmissions for the same QoS requirement as compared to a standard implementation of next hop selection algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".