IoT Hybrid Computing Model for Intelligent Transportation System (ITS)
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
IoT - a new proliferation in the technological advancement, changed the way object is perceived and used. It enables connecting smart objects to the internet and aims to develop new promising future to Intelligent Transportation System (ITS). ITS uses techniques such as wireless communication, computational technologies, GPS, and sensor technologies to provide smart and quick services to users and to be better informed and make safer, more coordinated, and 'smarter' use of transportation medium. As number of objects connected to ITS application increases, the amount of data generated also increases and they are send to cloud for data analysis and knowledge discovery. However, sending and retrieving of data across cloud is less useful due to delay latency and others. An alternative to cloud is fog (edge) model that overcomes the weakness of cloud by analyzing and discovering knowledge at the edge. However, the fog computing model has limited computational capability. For an IoT enabled Intelligent Transportation System with enormous number of objects connected, neither cloud nor fog computing model addresses the issues alone. This paper focuses on presenting an IoT hybrid model for Intelligent Transportation System (ITS). We also address the effectiveness of the model by discussing use case scenarios.
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 | 0.000 | 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 | 0.001 | 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