Energy-Efficient Parallel Singulation in RFID
Bibliographic record
Abstract
Tag collisions impose a significant hindrance to reading rates of Radio Frequency Identification systems. The parallel singulation approach, being a major milestone, clusters tags and autonomously interrogates each cluster in parallel. This technique reduces the number of tags being interrogated at a given time, reducing collisions, and achieves higher reading rates. However, such an approach faces two limitations as the number of clusters increase. The exponential increase in tag responses may hinder tag functionality due to energy spent on communication. Moreover, energy inefficiency is incurred at cluster-heads to process significantly more tag responses. These issues overshadow the promising benefits of employing parallel singulation. In this paper, we remedy such hindrances by proposing energy efficient enhancements to the parallel singulation technique. The essence of these enhancements lies in minimizing an important measure of communication overhead, referred to as tags traffic rate, which indicates the efficiency of interrogation cycles in communicating with all tags without incurring unnecessary overhead. Analyses carried out via simulation demonstrate significant improvements by the proposed schemes in reducing energy consumption of cluster-heads, without posing constraints on tag operations nor incurring significant degradation of reading rates.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".