A trust and reputation management system for cloud and sensor networks integration
Bibliographic record
Abstract
By incorporating the advantages of cloud computing (CC) and wireless sensor networks (WSNs), the integration of CC and WSNs attracts a lot of attention from both academia and industry. However, trust and reputation management for CC and WSNs integration is a critical and barely explored issue, which could strongly prevent the cloud service users (CSUs) from choosing the desirable cloud service providers (CSPs) or hinder the CSP from selecting appropriate sensor network providers (SNPs). To fill the gap, this paper proposes a novel trust and reputation management system for CC and WSNs integration. Considering the attribute requirement of CSU and CSP as well as the cost, trust and reputation of the service of CSP and SNP, the proposed system achieves the following two goals: 1) calculate and manage the trust and reputation regarding the service of CSP and SNP; 2) help CSU choose CSP and assist CSP in choosing SNP. Evaluation results are also shown to verify effectiveness of the proposed system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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".