Smart Solutions for RFID based Inventory Management Systems: A Survey
Bibliographic record
Abstract
This article is a survey of the latest technologies, algorithms and state of the art localization techniques that can be used to serve as Internet of Things communication protocol by automating an RFID system. There is a lack of a reliable and up-to-date reference that can help inventory management systems developers and operators to enhance the management system efficiency, maximize the productivity, and minimize the material loss. Several low cost IoT devices and associated technologies, such as Radio Frequency Identification system, are widely used today in several applications, including educational, transportation, animal tracking, inventory object tracking, and so many others. In this paper, we present a survey of the state-of-the-art technologies, algorithms, and techniques used in smart Radio Frequency Identification systems based inventory systems. We first outline the design challenges for RFID-based inventory management systems followed by a comprehensive survey of various RFID technologies, RFID types, and RFID architectures. In addition, the latest researches in the RFID infrastructure and middlewares are evaluated. This includes passive RFID Tags, RFID Antennas, RFID middleware, and the RFID Reader. Finally, the paper presents the advantages and performance issues of different techniques in passive RFID, and investigates the collision and anti-collision algorithms for these types of applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".