Low Information Redundancy Based Node Partition Protocols for Wireless Sensor Networks
Bibliographic record
Abstract
Coverage is one of the fundamental measurements of quality in wireless sensor networks. In order to prolong the network lifetime while maintaining coverage, many node partition algorithms have been developed. In this article, we model coverage problem by a set coverage problem. Based on the density of information, the optimal node partitions are investigated by solving an ILP problem. An intersection point method (IPM) is introduced to reduce the number of variables in ILP to O(km) where m is the number of deployed sensors; k is the number of neighbors. Even though the ILP model can give an approximately optimal solution for generating a minimum cover set. It cannot be used in a distributed scenario. Based on the Voronoi Diagram we present a distributed partition algorithm that constructs minimum node partitions by merging voronoi cells. The simulation results show that the IPM based ILP coverage model can deal with extremely large areas and improve the ILP performance by reducing the number of variables and constraints. The voronoi based distributed partition algorithm can give approximate optimal results as given by the IPM based ILP solution. Both the flexibility and accuracy of our algorithms show the potential to be used in scheduling and duty circle algorithms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".