Fault-Tolerance Mechanisms for Software-Defined Internet of Vehicles
Bibliographic record
Abstract
Due to the rapid development of the Internet of Things, cloud computing, big data, and software-defined networks technologies, vehicular ad hoc networks have evolved into software-defined Internet of Vehicles (IoV). In software-defined IoV, messages transmitted by vehicles are transferred using wireless communication technology, so attackers can easily attack specific vehicles and Road-Side Units (RSUs) within the wireless communication range. When any vehicle or RSU is under attack or control, the entire network may collapse and produce incorrect computing results, which in the worst case, can lead to car accidents. Therefore, building a highly reliable and fault-tolerant software-defined IoV is of high importance. In this paper, we visit the consensus problem in software-defined IoV. Software-defined IoV is built with infrastructures, that is, RSUs. RSUs have a backhaul capacity and a powerful computing capacity. Thus, in software-defined IoV, we will focus on how to use RSUs to assist vehicles in reaching a consensus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".