Power Usage of Energy Harvesting Sensors with a Drone Sink: A Reinforcement Learning Based Approach
Bibliographic record
Abstract
Wireless sensor networks (WSNs) can utilize radio frequency energy harvesting from ambient power sources for continuous operation, while drones acting as sink nodes increase the reliability of transmission and decrease a sensor's energy usage. In this paper, we attempt to optimize the transmit power of a sensor such that the overall outage probability is minimized. The sensors are assumed to have a finite-level battery and a buffer with discrete states. Energy is harvested from ambient energy arising from cellular base stations distributed according to a Poisson point process. The sensor's data is transmitted to a drone whenever its buffer becomes full. We consider two scenarios for the drone: i) hovering, and ii) moving on a fixed trajectory. Moreover, we utilize different path loss and fading models for the sensor-drone links due to their line-of-sight nature. An outage occurs due to both transmission outage and buffer overflow. We formulate the problem as a Markov decision process, and utilize a Q-learning based algorithm to learn the power control policy. Our numerical results show that the proposed policy significantly outperforms both the full and single-step energy usage policies. Moreover, it is robust enough to handle environmental/channel changes without a need for re-training.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".