Optimized Relay Placement to Federate Wireless Sensor Networks in environmental applications
Bibliographic record
Abstract
Federating Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM) becomes a necessity as advances in sensing technologies are achieved. Where several WSN sectors pursuing identical/different tasks intend to collaborate with each other in order to achieve more sophisticated and challenging missions, or intend to recover a significant damage in the network. Connecting (federating) these sectors is an intricate task due to the huge distances between the sectors, and the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes that provide vast coverage areas and sustain the network connectivity in harsh environments. However, these relays are expensive and thus, the least number of such devices has to be populated. In this paper, we propose a grid-based deployment for relay nodes in which the relays are efficiently placed on the grid vertices to connect the disjointed WSN sectors. Towards this efficiency, we design an Optimized Relay Placement (ORP) approach that maximizes the disjointed sectors connectivity while maintaining cost 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".