A Hierarchical Architecture for Distributed EPCglobal Discovery Services
Bibliographic record
Abstract
Efficient and scalable information discovery is one of the most important services in any large- scale Internet of Things (IoT) application, particularly in the EPCglobal Network. Although a number of distributed architectures have been proposed in the literature, both their scalability and their lookup time efficiency remain vulnerable, mainly because of their reliance on flat Peer-to-Peer (P2P) Networking. The purpose of this paper is to introduce a hierarchical distributed architecture for EPCglobal Discovery Services, called HEDSA, which improves the scalability and the lookup time of the flat P2P architectures, represented by FEDSA. The idea behind the hierarchy concept of HEDSA is that any Electronic Product Code (EPC) can be mapped to one and only one country, which is the issuing country of the corresponding company prefix. An emulation of FEDSA and HEDSA has been implemented on Planetlab using Chord algorithm, the objective being to compare the scalability and the lookup time of the two architectures. Several experiments have shown that HEDSA is much more efficient, both in terms of the number of hops and the lookup time, than FEDSA. Therefore, HEDSA is more suitable for large-scale IoT discovery services applications, such as the EPCglobal Network, provided that the identifiers can be mapped to one and only one geographical location.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".