Optimal Resource Allocation for Wireless Powered Sensors: A Perspective From Age of Information
Bibliographic record
Abstract
We investigate a wireless powered sensor network, in which multiple sensors generate data and send their data to a base station (BS) periodically. Each sensor first harvests energy from the BS via wireless power transfer and then uses its available energy to transmit to the BS its data. We target minimal average age of information, by optimizing the energy harvesting time and the bandwidth allocation during the sensors' transmissions. The research problem is hard to solve, as some notations in the problem do not have a closed-form expression. To optimally solve the problem, we first show that there is a one-to-one mapping from the energy harvesting time to the bandwidth allocation. We also develop a method to obtain the bandwidth allocation vector corresponding to each value of the energy harvesting time. Then we get the optimal energy harvesting time by investigating and comparing different sub-regions of energy harvesting time. Numerical results show optimality of our solution and its performance gain over a benchmark scheme based on the traditional threshold-based method.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".