Bibliographic record
Abstract
With Vehicular mobile communication becoming a daily requirement and an ever increasing number of services being available to passengers, it is clear that vehicular networks efficient communication systems. VANETs, one of the most significant trends in ad-hoc networking, has much to gain from improved content delivery and one of the leading contenders for mobile networks is the Information-Centric networking approach. Its peculiarities define the Vehicular Environment requires specialized solutions, tailored for highly mobile environments. The main contribution of this thesis is the introduction of a novel architecture and components. We perform extensively discuss Information-Centric Vehicular Ad-hoc Networks. Additionally, we perform an in-depth analysis of bus-based transit systems into VANETs not only as participating members but as service providers and official agents including roles and potential challenges. We perform statistical analysis and analyze world data to denote the intrinsic potential of public transit systems. From the discussions presented, we introduce a novel service-based system architecture for Information-Centric Networking named SEVeN. The proposed model is designed to enable service exchange and service management in highly competitive vehicular ad-hoc networks. The proposed SEVeN architecture includes the introduction of a novel purpose-defined naming policy and service sub-layer as well as a service prioritization policy named LBD. We also discuss the current state of ICN caching in VANET, existing issues faced by vehicular networks and potential approaches based on intermediate cache coordination that can be taken to mitigate existing shortcommings. We perform a series of simulations and analyze the efficiency of popular caching in various network configurations to denote current shortcomings. From this discussion, we propose a cache content insertion policies, UG-Cache and MG-Cache, for ICN-VANETs. In these cache policies, cache insertion decisions are made based on recommendations from content sender dependent on request frequency and cache distance. We also introduce a caching policy based on collaborative observation of locality in request frequency, designed to allow vehicles to preemptively distribute and store in a reserved portion of the cache based on the cooperative observation of requests with provider-based location correlation. All novel elements proposed by this thesis are discussed, described, evaluated within the chapters of this thesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".