A Web Services-Based Infrastructure for Traffic Monitoring Using ZigBee
Bibliographic record
Abstract
This paper presents our experience in building an infrastructure of combining ZigBee with Web Services for remote accessing and control of a network. A prototype traffic monitoring system was implemented based on this infrastructure as a proof-of-concept. However there is a much wider range of applications for this infrastructure. Currently, the infrastructure mainly concerns with the information delivery within the network. A multi-layer architecture was designed and implemented so that the infrastructure can be flexible and extensible for multiple applications. Within the infrastructure, the access points send data collected from the wireless sensor network to the application server. The data is then stored in a database on the application server for fast and reliable transactions. Users may then request certain data through a Web Service, or the data could be automatically delivered to the subscribers which are driven by specific events. The result of this preliminary work is an extensible application framework for information delivery over a large and low-cost ZigBee sensor network, and Web Services platform.
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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 | 0.000 | 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 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".