Radio Frequency Energy Harvesting and Data Rate Optimization in Wireless Information and Power Transfer Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it