Optimized Wireless Sensor Network Federation in Environmental Applications
Bibliographic record
Abstract
Federating partitioned Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM), where the deployed sensor nodes are prone to significant damage and harsh operational conditions, becomes a necessity to prolong the WSN lifetime. Consequently, redundancy-based deployment strategies have been extensively studied in the literature. However, federating WSNs using node redundancy is expensive in OEM due to large-scale targeted areas, and frequent node/link failures. A natural choice in defeating these challenges is to employ multiple Data Collectors (DCs) that provide extendable and sustainable WSNs in harsh environments for long lifetime intervals. In this paper, we propose a grid-based deployment for DCs in which they are optimally repositioning on the grid vertices to connect disjointed WSN sectors. Towards this optimality, we design an Optimized DCs Repositioning (ODR) approach that maximizes the federated WSN lifetime while maintaining cost and connectivity constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".