TASA: traffic offloading by tag-assisted social-aware opportunistic sharing in mobile social networks
Bibliographic record
Abstract
To solve the mobile traffic explosion problem, there have been many efforts to try to offload the mobile traffic from infrastructured cellular links to direct local short-range communications among users. In this paper, we propose a novel framework of traffic offloading by Tag-Assisted Social-Aware opportunistic sharing in mobile social networks, TASA, to offload traffic by device-to-device sharing. Based on the evaluation of the tags of users and contents, we select a subset of users who are likely to receive the same content as initial seeds depending on their spreading impacts in online SNSs and their mobility patterns in offline MSNs. Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, LTE D2D) with each other. The observation from SNS activities reveals that individual users have distinct access patterns, which allows TASA to further exploit the user-dependent access delay between the content generation time and each users access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TASA can reduce up to 78.9% of the cellular traffic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".