Deploying fault-tolerant grid-based wireless sensor networks for environmental applications
Bibliographic record
Abstract
In this paper, we propose two schemes for sensor and relay node placement in environmental sensing applications. The first scheme aims at maximizing the network lifetime by reducing the total energy consumption. The second does so while maintaining fault-tolerance constraints. It guarantees a lower bound on the minimum required number of faulty nodes. Both schemes are based on a 3-D hierarchical architecture, in which nodes are placed on grid vertices to limit the search space. We divide the lifetime of the network into fixed-length rounds and find the placement which reserves more energy in each round to prolong the lifetime. These problems are formulated via Integer Linear Programs (ILPs). An ILP solver is used to find the optimal placement of nodes in addition to multi-hop routing from the sensors to the base-station in both schemes. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant scheme introduces a significant lifetime extension as compared to the first one under the same harsh operational conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".