RF-Energy Harvesting from Moving Vehicles: Mathematical Modeling and Selection Protocol
Bibliographic record
Abstract
Wireless energy transfer can extend the lifetime of wireless sensor networks deployed in hard-to-reach and/or harsh environments. A number of works have proposed using dedicated mobile chargers (e.g., robots, drones, or vehicles) to transmit Radio Frequency (RF) energy to sensor nodes, which, despite optimal route selection, remains an expensive approach. In this article, we consider a scenario where sensor nodes could be recharged by passing vehicles. This could be the case of a wireless sensor network deployed to monitor the health of a bridge or a dam. We provide exact mathematical modeling of the harvested energy from a moving vehicle over T seconds and over a trip time. This model captures the effect of the vehicle's speed and location. Then, we extend the discussion to the case of multiple vehicles and propose a multi vehicle harvesting protocol. This protocol allows the sensor node to maximize its energy intake by proactively selecting the source vehicle. This work paves the ground for accurate designs of energy harvesting protocols that leverage surrounding sources of RF energy. The accuracy of the derived results is verified using computer simulations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".