Energy Efficient Cooperative Communications using Location based Relaying
Bibliographic record
Abstract
Geographic random forwarding (GeRaF) is a location based distributed routing protocol that allows wireless networks with aggressive sleep cycles of its nodes to deliver messages with low latency. Hybrid-automatic repeat request (H-ARQ) based intra-cluster geographically informed relaying (HARBINGER) is a protocol based on GeRaF with link-layer based adaptive error control techniques and promises better performance than GeRaF. In this paper we will study the performance of HARBINGER relative to GeRaF. We study the energies required for a message to reach its destination with a given latency bound, for both GeRaF and HARBINGER, with a practical high speed data packet access (HSDPA) based system. In particular, we highlight the different circumstances under which each protocol delivers a better energy efficiency. We propose simple modifications to the GeRaF protocol with which it can achieve the same or better performance as HARBINGER at lower node densities with lesser implementation complexity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".