Low RF Power Harvesting Enabled Wireless Sensor Node With Long-Distance Communication Capability
Bibliographic record
Abstract
It has been challenging for a wireless sensor node to realize long-range real-time information feedback in the low power harvesting scenarios, due to the limited power budget. In this article, a sensor node powered by low radio frequency (RF) power harvesting is proposed with long-communication capability without using an RF power amplifier or external power source. The proposed sensor node is capable of rectifying input RF power as low as −20 dBm at 433 MHz, while simultaneously generating a 866 MHz second harmonic during the harvesting process. To address problems of long-range data transmission, the generated harmonic is amplified by a tunnel diode powered by rectified dc power, while carrying sensor data of temperature, humidity, and CO2 concentration. The fabricated sensor node shows an output power of around −14.75 dBm for the second harmonic, even with an injecting RF power as low as −20 dBm. It enables a long range exceeding 40 m for indoor communication scenarios with a low energy consumption of just 20.7 mJ. The sensor node demonstrates a great potential for future internet of things (IoT) applications in low RF power harvesting scenarios.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".