A layered architecture for delay sensitive sensor networks
Bibliographic record
Abstract
Sensor networks are powerful tools for performing monitoring and surveillance tasks over large areas. A sensor is a cheap, simple device with low power and limited capabilities. In a sensor network a large number of sensors are deployed to span the whole area to be monitored. Due to the simplicity and the large quantity of the sensors involved, collecting data from a sensor network can be time and energy inefficient. In this paper, we investigate making the data gathering task from a sensor network more efficient by using a randomized, layered architecture. The layers in our architecture are constructed in a distributed fashion, with each sensor deciding locally on what layers it will exist. The key property of our technique is that the information is collected from one layer of the architecture containing a small subset of the sensors, resulting in fewer hops and thus smaller data in data aggregation. We provide provably correct results for the delay incurred and the accuracy of the results. In the context of our new techniques, we also explore ways to speed up the data gathering process even further, such as using history information. In addition, we consider how to optimize the structure of our system so that the energy consumption will be evenly distributed among each sensor, thus extending the overall lifetime of the entire network.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".