Linear approximation for energy and throughput optimization in Wireless Sensor Networks
Bibliographic record
Abstract
The focus of this paper is to analyze and assess the performance of various optimization techniques in the effort to determine the maximum lifetime of the nodes and the throughput in a Wireless Sensor Network (WSN) environment while being able to meet the network's application specific design requirements. We formulated the initial objective of the optimization problem as a nonlinear function and solved an instance of the problem in its original form using LOQO [7]. The objective function is then relaxed to assume simplified formulations based on certain assumptions in order to be solved using Geometric Programming (GP) and Linear Programming (LP) techniques with specific solvers, namely CVX and GGPLAB in Matlab. The main findings of this study reveal that there exists a tradeoff in the solutions obtained between performance accuracy and computational complexity. The resulting energy and delay minimization approach is shown to be able to support the various WSN application requirements.
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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".