Performance Characterization of Energy Harvesting Sensors with Limited Buffer and Battery Capacities
Bibliographic record
Abstract
Energy harvesting is a key enabling technology to prolong the lifetime of wireless sensor devices, while a sink node should be properly placed to efficiently gather sensor data. In our model, sensors distributed randomly based on a Matern cluster process harvest energy from a Poisson point process of base stations and use this energy for their transmissions to a stationary aerial sink such as a tethered drone. Moreover, each sensor is assumed to have a battery with multiple energy levels and a packet buffer with a finite capacity. We aim to comprehensively characterize the harvesting and outage performance of radio frequency energy harvesting sensor devices with an aerial receiver. We derive the ambient power at a sensor device using a moment generating function based approach, and the harvested energy is characterized. For the scheme where a sensor transmits whenever the buffer becomes full irrespective of the battery level, we derive the interrelated steady-state probabilities of the different battery states and packet buffer levels using Markov chains. Finally, the outage probability is derived when a sensor transmits its packets to an aerial sink node. Our numerical results illustrate that the numbers of energy and buffer states significantly impact the energy harvesting performance and the overall outage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".