Decentralized RFID Coverage Algorithms With Applications for the Reader Collisions Avoidance Problem
Bibliographic record
Abstract
We aim in this paper at eliminating data and reader redundancies in Radio Frequency Identification (RFID) reader networks. These redundancies have negative impact on the performance of an RFID reader network and in analyzing the readings of the network. We meet our objectives by introducing decentralized RFID coverage [reader collision avoidance (RCA)] algorithm. The RFID coverage problem consists of two subproblems: 1) the tag reporting problem, which aims at assigning to each tag in the network a reader responsible for reporting its data and 2) the redundant readers elimination problem, which aims at minimizing the number of readers in the network while preserving the tags coverage. We introduce two deterministic decentralized RFID coverage algorithms called orientation-based coverage and iterated orientation-based coverage (IOB-COVERAGE). The first algorithm runs in one communication round, whereas the latter runs in O(n) rounds, where n is the number of readers in the network. These algorithms are the first decentralized RFID coverage algorithms that use reader-to-reader communications only. We later introduce an algorithm that solves the RCA algorithm, called IOB-(RCA+COV). The algorithm is a minor modification of IOB-COVERAGE. We formally prove the correctness of our algorithms, and we use detailed simulation experiments to study their performance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".