Seasonally Aware Routing for Thermoelectric Energy Harvesting Wireless Sensor Networks
Bibliographic record
Abstract
Energy-aware routing schemes in wireless sensor networks (WSNs) often employ artificial energy assumptions, e.g., equal initial energy reserves for all nodes. Instead, we consider the case of realistic energy reserves collected via thermoelectric energy harvesting in an apartment complex and examine how the harvested energy impacts routing decisions over relatively large time frames. We formulate the corresponding multi-commodity routing flow problem and, using real observed data, remark that maximizing the volume of collected data typically leads to an uneven collection from each sensor. We propose a corresponding adjustment to the optimization problem to derive a “fair” data collection strategy. We additionally present a low overhead method of constructing a seasonally-aware routing scheme and study its performance. We compare the seasonally-aware routing performance against that of an ideal, centralized, optimization solution, as well as against a simple strategy to avoid extreme variance of residual energy at the sensor nodes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".