Enslavement of Wireless Sensor Network to an RF Energy Harvesting System
Bibliographic record
Abstract
The abundance of telecommunications systems makes it possible to have somewhat significant quantity of radiofrequency energy in the environment. This energy can be recycled to power ultra-low-power devices such as Wireless Sensor Network (WSN). In this paper, the performance of a miniature RF/DC converter is evaluated in order to enslave a WSN’s per-formance to the amount of the recovered energy. More precisely, a highly sensitive and efficient rectifier is designed to achieve optimum performance in the GSM band. The design method relies on a judicious choice of the rectifying diode which is the basis of most losses in a rectifying antenna (rectenna). Optimum performance is achieved by using the gradient method search proposed in the Advanced Design System (ADS) software. A rectifier based on Schottky diodes HSMS 2850 used in a voltage doubler topology is thus obtained. A maximum RF/DC conversion efficiency of 36% is reached for an RF input power level of 10 dBm. An energy budget of a sensor node in a WSN having an equitable distribution of network loads is then defined and used to evaluate the performance of the WSN regarding the distance at which the Base Station (BS) can be located. The Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for this purpose. The distance separating the WSN from the BS is used as the enslavement parameter. Our analysis shows that increasing the duration of each round results in an increase in the range of the WSN. As an example, a network with 100 nodes distributed over an area of may be located at 1.3 km from the base station when each node of the WSN must perform measurements every 1 min.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".