Toward a M2M-Based Internet of Vehicles Framework for Wireless Monitoring Applications
Bibliographic record
Abstract
The Internet of Vehicles (IoV) has become an attractive research topic in the fields of communication networks and information processing due to its wide range of potential applications, including public transportation management, road traffic predication and control, environmental monitoring, and autonomous driving. Most research analyze the IoV’s data collected by dedicated machine-to-machine (M2M)-based platforms. Moreover, due to the lack of field experimental data, data transmission mechanisms from mobile vehicle terminals to the M2M-based platform rarely consider the proper transmission timing according to network performance and traffic conditions. To solve these problems, we propose an open M2M-based framework of wireless monitoring system for IoV. In the proposed framework, we design and implement a prototype of the mobile vehicle terminal. We also define the procedure of data transmission between the mobile vehicle terminal and the M2M-based platform. To prolong the lifetime of the mobile vehicle terminal, energy-efficient data transmission schemes for stopped and running vehicles are proposed, respectively, which reduce the unnecessary data transmission by jointly considering the variation of vehicle speed and received signal strength. We conduct a field experiment to verify the basic functions of the proposed M2M-based IoV monitoring system and the mobile vehicle terminal. Furthermore, the experimental results show that the proposed M2M-based platform with the mobile vehicle terminal enables customized and energy-efficient data delivery.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".