A Q-Learning Based Transmission Management Strategy for Energy Harvesting Sensors
Bibliographic record
Abstract
Radio-frequency energy harvesting from ambient cellular energy and drone-based receivers close to the sensors can be effective tools to prolong the lifespan of wireless sensor networks (WSNs). Moreover, the energy management policy of a sensor plays a critical role in increasing the reliability of data transmission under severe energy constraints. Thus, in this paper, we develop an optimal transmission policy to reduce the outage such that a sensor decides on when to transmit and how much power to use given the uncertainties of the harvested energy. To this end, we model sensors with a finite-level battery and a buffer with discrete states, whereas the cellular base stations from which the energy is harvested are modeled according to a Poisson point process. The drone is assumed to be hovering at a fixed height above the field of sensors, and assume path loss and small scale fading with varying intensities depending on the line-of-sight nature of the link. An outage is assumed to occur due to incorrect reception of transmissions and buffer overflow. We formulate the problem as a Markov decision process, and utilize a Q-learning based algorithm to generate the optimal transmission policy. Our numerical results show that the proposed policy significantly outperforms the previously proposed policies under all conditions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".