A novel cost-effective architecture and deployment strategy for integrated RFID and WSN systems
Bibliographic record
Abstract
In this paper, we propose a novel architecture for integrated Radio Frequency IDentifiers (RFIDs) and Wireless Sensor Networks (WSNs) to accommodate an array of applications in a cost-effective manner. Integration combines the traceability and sensing capabilities of the two technologies to maximize the effectiveness of the resulting systems. RFID technology extends the ability of WSNs by tracking otherwise un-sensible objects. WSNs, on the other hand, provide information on the environment surrounding the node and the ability to transmit in multi-hops to wider areas. We propose a novel architecture to integrate these technologies via super nodes that serve as both RFID readers and relay hubs. The count of the super nodes dominates the cost of these integrated networks. Thus, it is crucial to distribute these nodes over the layout in a way that minimizes their count while ensuring coverage. We employ Integer Linear Programming (ILP) to achieve a deployment scheme that addresses such constraints. Our approach generated outstanding results in terms of cost-efficiency when compared to other WSN/RFID integrated architectures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".