Optimized relay placement for WSNs federation
Bibliographic record
Abstract
Advances in sensing and wireless communication technologies have enabled a wide spectrum of Outdoor Environment Monitoring applications. In such applications, several wireless sensor network sectors tend to collaborate to achieve more sophisticated missions that require the existence of a communication backbone connecting (federating) different sectors. Federating these sectors is an intricate task because of the huge distances between them and because of the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes (RNs) that provide vast coverage and sustain the network connectivity in harsh environments. However, these RNs are expensive; thus, the least possible number of such devices should be deployed. Furthermore, because of the harsh operational conditions in Outdoor Environment Monitoring applications, fault tolerance becomes crucial, which imposes further challenges; RNs should be deployed in such a way that tolerates failures in some links or nodes. In this paper, we propose two optimized relay placement strategies with the objective of federating disjoint wireless sensor network sectors with the maximum connectivity under a cost constraint on the total number of RNs to be deployed. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments. Copyright © 2011 John Wiley & Sons, Ltd. (Random in size (of SNs) and positions.)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".