Peer to peer content sharing on ad hoc networks of smartphones
Bibliographic record
Abstract
Peer-to-peer networks offer advantages over traditional client-server networking models, such as the lack of a need for connectivity to trusted intermediary hosts or servers and the use of less costly communication links. While they have become popular in the wired broadband environment, they have not yet been effectively adapted to the resource-constrained mobile network environment. They promise significant potential in applications such as the sharing of files like multimedia and operating system updates between mobile devices. However, the peer-to-peer model faces unique challenges in the mobile context, such as limitations on processing power, on-board device memory, wireless data bandwidth, and available battery energy. We propose a high-level framework for a peer-to-peer protocol with these specific constraints addressed. In addition, we investigate the feasibility of a practical implementation of a peer-to-peer file sharing model on smartphones, including an analysis of how performance is impacted by various variables that can be dynamically controlled in the protocol. Through experimentation on leading smartphones, we have found various optimal strategies, including minimizing the upload-to-download ratio to conserve battery life, using larger file segments to increase throughput, and using sockets to decrease memory overhead.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".