Autonomous Wireless Sensors Network Based on Piezoelectric Energy Harvesting
Bibliographic record
Abstract
Wireless sensor networks (WSNs) offer an attractive solution to many environmental, security and process monitoring. However, their lifetime remains very limited by battery capacity. Through the use of piezoelectric energy harvesting techniques, ambient vibration can be captured and converted into usable electricity to create selfpowering WSN which is not limited by finite battery energy. This paper investigates analytically and experimentally the performance of a WSN powered by a Piezoelectric Energy Harvesting System (PEHS) and a material block-level modeling considering most key energy consumption of a wireless sensor node in a star topology network is proposed. By using real hardware parameters of existing components, the proposed model is used to evaluate the energetic budget of the node. The sensor node performance is evaluated regarding transmit packet size, duty cycle and the number of nodes that can be deployed. From the spectral properties of the available vibration inside two moving vehicles (automobile and train), the maximal recoverable power for each type of vehicle is estimated. Using a PEHS based on a cantilever beam optimized for low-frequency applications, 6 mW power is recovered in the case of the train while a 12.5 mW power is reached in the case of the automobile. It is observed that the sink may not operate with the recovered energy. However, the sensor node can sense and transmit data with a maximum size of 105.5 kbits when the duty cycle is 4 × 10-15. It is also achieved that the node is most effective when the measured physical phenomena vary slowly, such as the variations in temperature due to thermal inertia. Considering an optimized PEHS based on non-linear processing, it is shown that the sink can operate for 190% improvement of the recovered power.
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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 | 0.000 |
| 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".