MR-Chord: A scheme for enhancing Chord lookup accuracy and performance in mobile P2P network
Bibliographic record
Abstract
In the recent years, Peer-to-Peer (P2P) sharing network has become very popular in the Internet. However, most P2P protocols are designed for traditional wired networks. When deployed in wireless network environment, many challenges are encountered. For instance, the nodes in an unstable wireless network tend to leave or rejoin the P2P network easily. In this case, the routing information in every node must become overdue, which may lead to lookup failures when the nodes retrieve these overdue routing information. In this paper, we propose a modified Chord protocol called MobileRobust-Chord (MR-Chord). MR-Chord is designed with the aim of keeping the Finger Table fresh. To achieve this goal, we have modified the Distributed Hash Table (DHT)-based protocol a Chord Protocol in such a way that the Finger Table is kept updated to provide the necessary lookup services in the P2P network. Simulations studies show that our proposed MR-Chord protocol outperforms the original Chord protocol in the following aspects: (1) increase in the lookup success rate and overlay consistency, (2) reduction of the lookup delay time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".