Survey on Social-Aware Data Dissemination Over Mobile Wireless Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data dissemination finds a wide range of appealing applications in disaster alert, event notification, and content distribution. In particular, with the evolution of mobile networks and the popularity of online social networks, mobile social networks (MSNs) offer a promising paradigm to facilitate data dissemination. Traditional data dissemination approaches focus on how to leverage the resources in the physical networks, such as opportunistic contacts in delay tolerant networks and opportunistic networks, or the infrastructure in the cellular networks. In contrast, social-aware data dissemination approaches also exploit the valuable information from the social networks and take into consideration the complex requirements of human users. A systematic review of the existing approaches for data dissemination can provide insightful information and motivate more in-depth studies in this area. In this paper, we first review some traditional approaches as a basis for comparison. Then, we introduce some fundamental background on MSNs, device-to-device (D2D) communication, game theory, and matching theory, which have been used in existing studies on social-aware data dissemination. The technical and mathematical information is helpful for readers to follow our discussions in the main body of this paper, which surveys many social-aware approaches in the literature. We group our discussions based on the theoretical models for various problems in data dissemination. Also, we separate the problems, initial source selection and incentive design, from others to emphasize their importance. In the end, we highlight some interesting research directions for future study on data dissemination.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.001 |
| 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 it