Probabilistic Energy Optimization in Wireless Sensor Networks with Variable Size Griding
Bibliographic record
Abstract
Due to limited energy supplies, reducing power consumption is an important goal in wireless sensor networks. Clustering techniques are used to reduce power consumption and prolong network lifetime in many existing research efforts, among which grid-based ones are often used due to their simplicity and scalability. However, most existing work uses average distance as a simplification in calculating distance-related power consumption, which leads to a large underestimation of the actual energy depletion rate. In this paper, we propose an energy optimization model based on probabilistic distance distributions, which captures the distance-induced power consumption with high accuracy. We further analyze the uneven traffic distribution in wireless sensor networks and propose a nonuniform griding scheme to balance the energy depletion in all grids. Through our analysis, we are able to obtain the optimal grid size ratio that minimizes the energy consumption. Analytical results are validated through simulation, which shows the promising potentials of our method and the nonuniform griding technique.
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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 | 0.001 | 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".