Efficient Approach for Redundant Reader Elimination in Large-Scale RFID Networks
Why this work is in the frame
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Bibliographic record
Abstract
Radio Frequency Identification (RFID) systems, due to recent technological advances, are being deployed in large scale for different applications. However, this requires a dense deployment of readers to cover the working area. Without optimizing reader's distribution and number, many of them will be redundant, reducing the efficiency of the whole RFID system. The problem of eliminating redundant readers has motivated researchers to propose different algorithms and optimization techniques. In this paper, the authors present an efficient redundant reader elimination technique based on weights associated with reader's neighbor and coverage. Simulation results demonstrate that the proposed algorithm eliminates more redundant readers than those of other well-known techniques like Redundant Reader Elimination (RRE) algorithm, Layered Elimination Optimization (LEO) and LEO+RRE while preserving the coverage ratio quite close to those obtained by RRE, LEO and LEO+RRE.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it