Protocols for Data Propagation in Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
Recent dramatic developments in micro-electro-mechanical systems (MEMS), wireless communications and digital electronics have already led to the development of small in size, low-power, low-cost sensor devices. Such devices integrate sensing, data processing and communication capabilities. Examining each such device individually might appear to have small utility, however the effective distributed co-ordination of large numbers of such devices may lead to the efficient accomplishment of large sensing tasks. Large numbers of sensors can be deployed in areas of interest (such as inaccessible terrains or disaster places) and use self-organization and collaborative methods to form a network. Their wide range of applications is based on the possible use of various sensor types. Thus, sensor networks can be used for continuous sensing, event detection, location sensing as well as micro-sensing. We note however that the efficient and robust realization of such large, highly-dynamic, complex, non-conventional networking environments is a challenging technological and algorithmic task. Features including the huge number of sensor devices involved, the severe power, computational and memory limitations, their dense deployment and frequent failures, pose new design, analysis and implementation challenges. This chapter aims at presenting certain important aspects of the design, deployment and operation of sensor networks. In particular, to provide a) a brief description of the technical specifications of state-of the-art sensor devices b) a discussion of possible models used to abstract such networks c) a presentation of some characteristic protocols for data propagation in sensor networks, along with an evaluation of their performance analysis.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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