A Distributed Graph-Based Dense RFID Readers Arrangement Algorithm
Bibliographic record
Abstract
Radio Frequency Identification (RFID) plays a key role in the Internet of things (IoT). The type of scenario that needs to use many readers to cover a large area is a dense RFID environment scenario. In supply-chain management, companies such as Wal-Mart use dense RFID reader systems to track products [1]. Collisions usually happen in dense RFID reader systems, which reduce the number of tags that can be read by the system. Many algorithms were designed to eliminate the collisions in a dense RFID environment. A Maximum-Weight-Independent-Set-Based Algorithm (MWISBA) [2] is used to solve the dense RFID readers' arrangement uses a graph-based algorithm to get the MWIS. However, MWISBA does not consider interference range, it can only avoid reader-to-tag collisions. Based on MWISBA, an improved algorithm called MWISBAII [3] can avoid both reader-to-tag collisions and reader-to-reader collisions. However, both MWISBA and MWISBAII are centralized algorithms. In this paper, we propose a distributed realization of MWISBAII. In our distributed algorithm, each reader can communicate with other neighbor readers to share and collect information; making the local decision afterwards. The experimental results show that our distributed algorithm can get almost the same performance as the MWISBAII.
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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 | 0.000 | 0.002 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".