MR-Chord: Improved Chord Lookup Performance in Structured Mobile P2P Networks
Bibliographic record
Abstract
Peer-to-peer (P2P) networks are becoming very popular since various applications such as media streaming and voice over IP use these networks in different environment settings without the need for a client-server structure. P2P protocols have been originally designed for traditional wired networks, and when deployed in wireless network environments, several challenges are encountered. For instance, P2P clients may depart or join the network frequently, raising the issue of identification and retrieval of data items in an efficient manner. In this scenario, the routing information in P2P clients may become overdue, leading to lookup failures. This paper continues the investigation of our recently proposed solution for Chord lookup in mobile P2P networks [so-called mobile robust Chord (MR-Chord)]. MR-Chord was designed to maintain and update the finger table using a modified distributed hash table-based protocol, so that the necessary lookup services in the network are provided. Our contribution consists in studying the effects of node mobility on the performance of MR-Chord. Simulation results show that in the presence of node mobility, MR-Chord outperforms the original Chord protocol in terms of lookup success rate, overlay consistency, lookup delay time, lookup hot count, and total network load, chosen as performance metrics.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".