WSN Architectures for Intelligent Transportation Systems
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 emergence of, and advancement in, integrated digital circuitry technology along with the development of efficient software algorithms made it possible to build small, lightweight wireless nodes equipped with embedded processors, sensors and radio transceivers. By densely deploying these nodes, in a highly distributed manner, we can build a Wireless Sensor Network (WSN). In this network, sensors collaborate in monitoring physical parameters or environmental conditions, such as temperature, sound, vibration, etc. Sensor nodes frequently report the data they capture to a central collection unit that analyses the data and takes appropriate actions if needed. Intelligent Transportation Systems (ITSs) emerged as a potential candidate for benefiting from the unique features and capabilities of WSNs. In ITSs, transportation infrastructure is supported with the ingenious achievements of computer and information technology to resolve severe situations like traffic congestion and cope with emergency conditions like major accidents. In this paper, we study the requirements for an efficient WSN architecture for ITSs, survey the WSN architectures proposed for this type of applications highlighting their strengths and weaknesses, and shed light on future directions in this field of research.
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 | 0.000 | 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