Scalable Delay-Sensitive Polling of Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a sensor-rich Internet of Things environment, we may be unable to gather all data at a processing centre at the rate at which the data is generated. The rate of data collection from a sensor may be limited by available bandwidth/cost (or energy considerations), especially if one were to use cellular networks for such systems. In this context, we present a mechanism for determining which sensors to gather data from at each polling epoch. Our sensor polling mechanism prioritizes sensors using information about the data generation rate, the expected value of the data as well as its time sensitivity. Our problem formulation and its solution relate to the restless bandit model for sequential decision making. Whereas existing methods for the restless bandit model are not directly applicable because the state space is continuous and not discrete, we prove that similar techniques can be used because of particular characteristics of the underlying problem. We then show that our approach can be very effective even when not optimal through an extensive quantitative study where event arrivals follow a hyper-exponential distribution.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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