Bibliographic record
Abstract
In recent years, mobile ad hoc networks (MANETs) have become popular in different areas.In MANETs, mobile nodes can join or leave the network freely.Because of the mobility of nodes and open wireless medium, MANETs are vulnerable to security attacks.In this thesis, we propose a novel framework of software-defined MANETs with trust management.Specifically, we separate the forwarding plane in MANETs from the control plane, which is responsible for the control functionality, such as routing protocols and trust management in MANETs.Using the on-demand distance vector routing (TAODV) protocol as an example, we present a routing protocol named software-defined trust based ad hoc on-demand distance vector routing (SD-TAODV).Simulation results are presented to show the effectiveness of the proposed softwaredefined MANETs with trust management.iii First of all, I would like to sincerely thank my supervisor, Professor F. Richard Yu for his tremendous time and efforts spent in leading, supporting and encouraging me in the course of my thesis and study.It would not be able for me to finish my thesis without his help and I will always keep to follow his instructions and inspiration to my professional career.I would also thank my colleagues for their understanding and friendship during the time we spent together.Finally, I would like to
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".