Bibliographic record
Abstract
Peer-to-peer (P2P) applications are enormously popular on the Internet. Their uses vary from file sharing to Voice-over-IP to gaming and more. Increasingly, users are moving toward wireless networked devices and wish to continue using P2P applications in these new environments. A mobile ad hoc network (MANET) is an infrastructureless network which allows users to dynamically form a mobile, wireless network. Though P2P and MANETs share some similarities, such as self-organization, dynamism, and resilience to failure, it is necessary to create new P2P algorithms that take advantage of the realities of MANETs. These algorithms must account for the numerous challenges found in these networks, including node mobility, resource constrained nodes, and the necessity of fully distributed algorithms. In this thesis, we propose a framework for mobile P2P computing in MANETs (P2P-MANETs). Our proposal includes the following components. First, nodes must be able to locate and join the P2P overlay. We therefore propose a fully distributed bootstrapping algorithm in which nodes multicast join requests and cache responses. Next, the overlay peers must form a topology of connections between themselves. We propose a fully distributed topology control heuristic which supports the dynamic nature of the P2P-MANET. It is important that peers contribute to the network by sharing their resources and forwarding traffic for others. We therefore propose a dynamically priced incentive scheme which rewards users for contributing to the network. We also propose a path selection algorithm to allow peers to select how many parts of a file to download from which servers and which paths to satisfy the user’s preference for download time and cost. Finally, we propose a content distribution system that allows users to download large files through the use of network coding and multicasting. Each of these components is the first proposed for its respective place in a P2P-MANET architecture. Simulation results show that each of the proposed components achieves the goals set out for it and outperforms the comparison schemes. The results also show that the overlay topology and path selection heuristics provide good approximations compared to the optimal solutions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".