An Efficient ZigBee-WebSocket Based M2M Environmental Monitoring System
Bibliographic record
Abstract
Technologies to support the Machine-to-Machine (M2M) is becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as wireless network, will not be isolated but connected and integrated composing computer networks. So far, to enable an End-to-end M2M service, WebSocket has attracted lots of attentions because of its unique full-duplex communications features. Besides, ZigBee technology has widely been deployed in short-range wireless communication systems with its low-power dissipation and high transmission speed. In this paper, we focus on the emerging M2M gateway development for home and industry applications. Specifically, by providing the detailed system architecture and user cases, we give a specific analysis on environmental monitoring implemented with WebSocket and ZigBee technology. The ZigBee sensor network is used to collect the temperature and humidity information. The foreground of the system shows the related data through B/S (Browser/Server) mode by utilizing WebSocket to push the information received by a web server to the client browser.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.002 |
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".