MobiShare: Efficient Decentralized Data Sharing for Mobile Devices
Bibliographic record
Abstract
Existing peer-to-peer data-sharing methods suffer from low data delivery efficiency and scalability due to the naive data request/response procedure and the high redundant data transmission rate. It becomes even worse in large-scale mobile networks considering the limited resources of mobile devices. To address this issue, this paper presents MobiShare, an efficient decentralized data-sharing approach for mobile devices, which allows users to not only share the data but also the data generation methods. To achieve MobiShare, we introduce a function block encoding method and a data request method to enhance sharing efficiency, minimizing costs for decentralized data sharing. We propose a credit payment mechanism where congested devices can send data vouchers instead of actual data, containing the expected transmission time. Based on the load and bandwidth of devices, we build the optimized dissemination tree with data vouchers in a decentralized way to improve scalability. Evaluation results show that MobiShare avoids redundant transmission. It greatly shortens transmission completion time and lowers energy consumption with limited network resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.004 |
| 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".