A Comparison Study of Connected Vehicle Systems between Named Data Networking and IP
Bibliographic record
Abstract
Connected vehicle (CV) system has been a very promising technology to improve transportation safety and efficiency, which is a crossing field of intelligent transportation systems (ITS) and the internet of things (IoT). The popular IP-based network protocol has been employed in most CV applications. However, the host-tohost feature of IP protocol is low-efficient for information dissemination. Further more, IP protocol is limited to support direct communication among vehicles with highly ad hoc connectivity. Recently, the next generation network, Named Data Networking (NDN), has been proposed to be a very promising technology to address the information dissemination problem of IP protocol, particularly in cloud computing-based applications and services. In this paper, SimIVC-NDN, a federated simulation platform with the capability of performing a microscopic traffic simulation with both NDN and IP-based networking is proposed. With SimIVC-NDN, we have conducted a quantitative simulation comparison of two CV systems powered by NDN and IP protocols respectively for image dissemination, a common cloud computing service. In the experiments CV systems are constructed based on a calibrated traffic model of Whitemud Drive at Edmonton, Canada. The simulation results show that the NDN-based CV system lowers the packet delay by two orders of magnitude compared with the IP-based one with low packet loss rate, indicating that a NDN-based networking is a promising alternative to the conventional IP-based one for cloud computing applications of CV 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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".