Sensor network using Power-over-Ethernet
Bibliographic record
Abstract
A generic sensor network architecture is developed, combining Power-over-Ethernet (PoE) and Internet-of-Things (IoT) concepts. Distributed sensor nodes consist of a local controller (Raspberry-Pi board), to pre-process and format data collected from several sensors, and an Ethernet and power management circuit, to interface with PoE-enabled Ethernet ports. The system design follows a client-server architecture: by combining PoE with the IoT, the endpoint clients and servers (PoE controller modules) can communicate in local area network using the Constrained Application Protocol (CoAP). Clients can send commands to observe sensor data, change data acquisition sampling rate, and post new sensor resources. As data gets observed by clients, it can be further stored in a custom-designed HDF5 (Hierarchical Data Format 5) file format, for post-processing, visualization and data management. The convergence of data and power flow control enables a smart power management at the network level - most of the sensor nodes will be powered-up (awoken) only after custom triggering events detected by a main subset of always-on sensing nodes. These PoE controller modules are intended for structural and environment monitoring in one of the tallest wooden tower in the world, to be built in UBC campus. The generic architecture may be further adopted in the future for other types of applications, like environmental monitoring or human health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".