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 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".