Location information dissemination scheme for RFID-based distributed localization systems
Bibliographic record
Abstract
The availability of location information is essential for context- and location-aware services, which are typically provided by a large number of applications. RFID systems are extensively utilized to provide localization service typically through a centralized and coordinated approach. In this paper, we propose a distributed location information dissemination scheme using heterogeneous uncoordinated mobile RFID readers with the support of inexpensive "memory spots". In the proposed scheme, mobile RFID readers localize passive RFID-tagged objects and leverage the available memory spots in a given smart environment to disseminate location information. Mobile RFID readers use such memory spots to store tag locations and queries enabling exchange location information without the need for direct communication among each other. We study the behavior of the proposed scheme and compare its performance with a typical pull dissemination strategy through extensive simulations using ns-3. Our results indicate that the proposed scheme outperforms the typical pull dissemination strategy in terms of localization delay and average overhead under different dynamicity settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".