Efficient Multiway Relaying for Data Sharing in Energy Harvesting Sensor Networks
Bibliographic record
Abstract
In a wireless sensor network (WSN), sensors often need to share their measurements for applications like distributed estimation and detection or data aggregation. Here, we suggest using multiway relaying (MWR) for data sharing between energy harvesting sensors that cannot directly communicate with each other. We first start by studying the achievable data rate of amplify-and-forward (AF) MWR for energy harvesting sensors. Then, we show that, by backing off the transmit power at the sensors, not only better energy efficiency and longer lifetime are achieved, but also the data sharing rate increases. Based on this result, we further improve the performance of AF MWR in the assumed WSN by smartly adjusting the transmit power at the sensors. Our power allocation is devised in a way to improve the energy efficiency of MWR and increase the sum rate of data sharing between the sensors over the network lifetime. Simulation results are presented to verify the enhancement achieved by using our proposed power allocation technique.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".