An optimal energy allocation algorithm for energy harvesting wireless sensor networks
Bibliographic record
Abstract
With the use of energy harvesting technologies, the lifetime of a wireless sensor network (WSN) can be prolonged significantly. Unlike a traditional WSN powered by non-rechargeable batteries, the energy management policy of an energy harvesting WSN needs to take into account the energy replenishment process. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor node aims to maximize the total throughput in a finite horizon subject to time-varying energy harvesting rate, energy availability in the battery, and channel fading. We formulate the energy allocation problem as a sequential decision problem and propose an optimal energy allocation (OEA) algorithm using dynamic programming. We conduct simulations to compare the performance between our proposed OEA algorithm and the channel-aware energy allocation (CAEA) algorithm from [1]. Simulation results show that the OEA algorithm achieves a higher throughput than the CAEA algorithm under different settings.
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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 | 0.000 | 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 | 0.001 |
| 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".