On the Partial RSS-Connectivity Based Localization in Wireless Sensor Networks
Bibliographic record
Abstract
This paper studies the impact of using partial connectivity information in a received signal strength (RSS) based localization method in wireless sensor networks (WSNs). It aims to find the degree of neighborhood sufficient and necessary to improve localization based on RSS technique. A hybrid approach, based on using jointly the partial connectivity information and the RSS is used. The purposes are evaluating the estimated path loss exponent (PLE) of the propagation model and the positions of unknown nodes. Extensive simulations are conducted to evaluate the normalized mean absolute error (NMAE) of the PLE of the channel using global connectivity and partial connectivity at two different neighborhood levels. Also, cumulative distribution function (CDF) of the NMAE localization compared to Cramer-Rao lower bound (CRLB), using partial and global connectivity are presented. Obtained results show that sufficient accurate estimations can be achieved when exploiting connectivity information at only 2-hops neighborhood, allowing reduce the information exchange.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".