Cooperative ARQ-Based Energy-Efficient Routing in Multihop Wireless Networks
Bibliographic record
Abstract
In this paper, we design an integrated protocol that jointly optimizes the performance of the physical, medium access control (MAC), and network layers. Our goal is to minimize total network energy consumption while delivering a minimum required signal-to-noise ratio (SNR) at each intended receiver within the network. At the MAC layer, we develop a cooperative automatic repeat request (ARQ) system, in which the relay nodes assist the source with its retransmission attempts. A complete analytic framework for the cooperative system is developed. Using this framework, we find the optimum transmission energy at the physical layer. To demonstrate the effectiveness of the proposed scheme in minimizing energy consumption, we propose two cooperative routing algorithms at the network layer. The proposed algorithms utilize the derived cooperative link cost as a basic building block. Through analysis and simulations, it is shown that over a single hop, cooperative transmission can achieve energy savings of up to 73%, as compared with noncooperative transmis.sion. The simulation results also demonstrate that the proposed routing algorithms can achieve significant energy savings while using fewer hops, as compared with the baseline cooperative and noncooperative routing algorithms.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".