Localization using multicarrier communication systems for Wireless Sensor Networks
Bibliographic record
Abstract
A wireless sensor network (WSN) consists of spatially distributed autonomous tiny devices (nodes) using several sensors to cooperatively monitor physical or environmental conditions, such as temperature, lighting, sound, vibration, pressure, motion or pollutants, at different locations. Localization is very important for self-configuring WSNs and is essential to properly process the sensed data. In this paper, we discuss the special design considerations for WSN localization based on MultiCarrier (MC) communication systems. The Cramer-Rao Bound (CRB) is compared between the different ranging measurement techniques used in cooperative localization. We introduced Selective Duplication Technique (SDT) to optimize the MC system from WSN perspectives. SDT is considered as an extension to the sub-bands Duplication Technique (DT). The CRB for DT and SDT is examined to reflect the figure of merits achieved using the proposed SDT technique. Simulation results show significant performance improvements in localization accuracy using the proposed SDT technique.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".