Bibliographic record
Abstract
In this dissertation, we investigate performance improvements in software-defined and virtualized vehicular ad-hoc networks (VANETs) with advanced technologies.We firstly present a deep reinforcement learning (DRL) approach in software-defined vehicular ad-hoc networks with trust management.In this research, we propose to use a deep Q-learning (DQL) approach and the centralized control mechanism of SDN to address the bad influences of the malicious nodes in VANETs.The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function.DQL is used to obtain the best link quality policy under the bad influence of the malicious vehicles in the inter-vehicle communication for ITS.Specifically, by decoupling the control layer from the device layer, the DQL algorithm is used in a logically centralized controller in the control plane of the proposed scheme.Secondly, we focus on distributed SDN and blockchain technology for connected vehicles in smart cities.Specifically, we propose a novel blockchain-based hierarchical distributed software-defined VANET framework (block-SDV) to establish a secure and reliable architecture that operates in a distributed way to overcome the security issues of VANETs.In order to achieve the goal of maximizing the system throughput and to ensure the trust information is not tampered with, we propose a blockchain-based consensus protocol that interacts with the domain control layer with the blockchain system.We aim to use this consensus protocol to securely collect and synchronize iii To pen down this section of my dissertation means I have almost arrived at the destination that I had dreamed every night.This exciting and unbelievable journey started when I wrote my first email to my supervisor, Prof. F.Richard Yu.Thus, I would like to begin this section by thanking him.Without his invaluable support and wonderful supervision, this would have been impossible.His technical insight and on-going encouragement have been a constant source of the motivation.The innumerable discussions with him and his ideas on the research projects have been the most dispensable input for my research.His comments and suggestions on my works not only increase the quality of the research but also provide a source of thought for my career.This work is as much his contribution as mine for the fact that he has always
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".