A Novel Data Collector Path Optimization Method for Lifetime Prolonging in Wireless Sensor Networks
Bibliographic record
Abstract
Due to the limited battery capacity, the lifetime and performance of the battery-powered WSNs are constrained. In order to prolong the lifetime, applying mobile data collectors to gather data in WSNs is a promising approach. In this paper, we design a two-phase data gathering strategy with the mobile data collector in the cluster-based WSN to improve energy efficiency and satisfy the delay constraints. More precisely, the sensors are divided into a set of clusters in the first phase, which ensures that the sensors can communicate with the mobile data collector within predetermined hops. We then develop the path for the mobile data collector using a genetic algorithm that is an applicable strategy for the optimization problem with respect to the shortest path finding in the large-scale WSNs. We evaluate the performance of the proposed path planning protocol by conducting intensive simulations. The simulation results indicate that the proposed scheme outperforms some state-of-the-art techniques on energy efficiency while enhancing the data update rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".