User‐centric context data collection and provision harnessing Content‐Centric Networking paradigm
Bibliographic record
Abstract
SUMMARY A comprehensive context management approach is necessary in the era of ubiquitous technologies, and efficient context data collection is one of the most fundamental and important processes for realizing comprehensive context management. Traditional context data collection approaches are based on Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) over Internet Protocol (IP), which has several disadvantages, such as lack of efficient mobility support, security and data transfer efficiency. Content‐Centric Networking (CCN), on the other hand, provides advantages in terms of mobility, security and bandwidth efficiency in comparison with IP. In this paper, we introduce our user‐centric comprehensive context management framework, and propose a secure and efficient context data collection and provision approach based on the framework using CCN as a network and transport layer. This context collection approach provides a flexible security mechanism by introducing three levels of security type. It also provides bandwidth efficiency by taking advantage of CCN's content caching; performance evaluation results show that our approach can reduce bandwidth consumption up to 99% for pull and up to 46% for push in comparison to a UDP/IP‐based system. Our approach also provides advantages in supporting mobility and leveraging multiple interfaces. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".