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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".