Survey on Social-Aware Data Dissemination Over Mobile Wireless Networks
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.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".