Signal Detection Techniques in Social Internet of Vehicles: Review and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Social Internet of Vehicles (SIoV) merges social networking and Internet of Things technologies within the automotive domain, facilitating real-time communication and data sharing among vehicles. Ensuring the dependable transmission of signals in the SIoV is important as it significantly enhances traffic efficiency, safety, and user experience. Signal detection techniques play a vital role in guaranteeing the reliability of various signals, encompassing traffic signals, vehicle statuses, and road conditions, transmitted through diverse vehicle-to-everything (V2X) communication channels. This article initiates by exploring the evolution of vehicle networking, signal transmission scenarios, and network structures within the SIoV. Subsequently, a comprehensive review of signal detection technologies in the SIoV are provided. Furthermore, we discuss the future challenges that pertain to SIoV signal detection techniques. The objective of this article is to provide guidance for the development of SIoV signal detection technologies that provide robust communications in intelligent transportation systems.
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.
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 | 0.001 | 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 it