Radio Frequency Energy Harvesting and Data Rate Optimization in Wireless Information and Power Transfer Sensor Networks
Bibliographic record
Abstract
Wireless energy harvesting using radio-frequency (RF) energy is a growing area of research to power in- and/or on-body sensors. However, solutions currently proposed in literature are hard to realize in a dynamic environment representative of the real world. This paper proposes the use of multiple intended RF sources with a harvest-then-transmit protocol to maximize the harvested energy and optimize data rate in wireless information and power transfer sensor networks. The problem of optimizing system timings to simultaneously maximize the harvested energy and network-level achievable data rate is tackled using optimization theory in concert with an RF source selection algorithm for the energy harvesting sensor nodes. With the methods proposed in this paper, it was found that the system achievable data rate and throughput fairness when energy is harvested from up to 5 RF sources can increase by up to 87% and 50%, respectively, compared with solutions when energy is harvested from one source. The proposed algorithm can also increase the system achievable data rate and throughput fairness by up to 72% and 22%, respectively, compared with a system without the algorithm. The findings are significant for designing and realizing future generation sensors powered by energy from multiple intended RF sources in the real world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".