Test-Bed for Sensor Network Management Protocols
Bibliographic record
Abstract
An implementation of a sensor network test-bed is presented in this paper. This test-bed will be used for performance benchmarking of existing and novel sensor networks management protocols. The sensor networks are unique as much as they are common. From one point of view, we see that sensor networks share many attributes with other networks having the same architecture or communication type. Thus a study of sensor networks can not but overlap with other areas of research. From another point of view, there are certain characteristics of sensor networks that differentiate them from others such as the high density of sensor nodes that can surpass the 20 nodes/m3in some of the cases and lead to the need for appropriate management protocols for cooperation of these nodes. This paper deals with building a test-bed that consists of a PC which manages the network through sensor management graphical user interface (GUI) and communicates with the sensor nodes through a base station, which is implemented by combining a FPGA-based Nios II Development kit, Cyclone Edition serially connected to a MIB510 equipped with a MICA2 node with ID0
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".