Recent advancement in sensor web architectures and applications
Bibliographic record
Abstract
Wireless Sensor Networks (WSN) consist of thousands of spatially distributed, low cost, low energy, unattended, and resource constrained sensor nodes for environmental monitoring, pollution detections, battle field surveillance etc. A Sensor Web (SW) is a web-based WSN, where a web application works as a gateway between the WSN and Internet. The Web interface is connected to the World Wide Web or huge computing resources and integrates sensor data and networks. Two major SW architectures are Open Geospatial Consortium defined Sensor Web Enablement (SWE) and Microsoft defined SenseMap. On the other hand, SW is used in academic purposes, agriculture, traffic monitoring, disasters monitoring, and smart home etc. There are many other novel applications as well as middleware for SW. Moreover, researchers from different fields give emphasis on different aspects of SW, while combining these two networks: sensor and web. They focus on strategies and technical issues of SW as well the utilization of sensor data by distributing it through the Web. In this paper, we discuss and compare existing SW architectures. We also present several SW applications and classify them with some potential research issues in this field.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".