RELMA: A Range Free Localization Approach Using Mobile Anchor Node for Wireless Sensor Networks
Bibliographic record
Abstract
Efficient sensor localizations are techniques for efficiently identifying sensors' positions for different Wireless Sensor Networks (WSNs) applications (e.g., environmental monitoring). Most existing localization techniques are designed for low scale sensor networks. Moreover, existing localization approaches are mostly range based that use some powerful nodes equipped with expensive GPS and/or extra hardware (or techniques) for distance estimations. On the other hand, most researchers focus only on the energy efficiency of sensor networks when designing localization method though 1) cost, 2) accuracy, and 3) scalability should also be considered as major design factors. In this paper, we propose Range-free Energy efficient, Localization technique using Mobile Anchor (RELMA) for large scale WSNs that improves accuracy and energy efficiency by reducing the number of anchor nodes. Simulation results demonstrate these properties where RELMA Outperforms NBLS an existing localization approach in terms of localization accuracy and energy efficiency. Moreover, rigid statistical analysis is used to validate these results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".