Mobile traffic offloading by exploiting social network services and leveraging opportunistic device-to-device sharing
Bibliographic record
Abstract
The ever increasing traffic demand is becomming a serious concern for mobile network operators. In order to solve the traffic explosion problem, there have been research efforts on offloading the traffic from cellular links to local communications among users. In this article, we leverage opportunistic device-to-device sharing, exploit the social impact among users in social network services (SNSs), and propose the novel framework of traffic offloading assisted by SNSs via opportunistic sharing in mobile social networks, called TOSS. In TOSS, initially a subset of mobile users are selected as seeds depending on their content spreading impact on online SNSs and their mobility patterns in offline MSNs. Then users share content objects via opportunistic local connectivity (e.g., WiFi Direct) with each other. Furthermore, the observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to utilize the user-dependent access delays between the content generation time and each user's access time for opportunistic sharing purposes. By trace-driven evaluation, we demonstrate that TOSS can reduce by 63.8-86.5 percent of cellular traffic while satisfying the access delay requirements of all users. Therefore, traffic offloading by leveraging opportunistic deviceto- device sharing based on SNSs can be quite effective and efficient as a promising content delivery service for traffic reduction in future mobile networks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".