Optimal sensing using query arrival distributions
Bibliographic record
Abstract
We examine optimal strategies for sampling and querying a sensing system when energy and data freshness need to be balanced. This approach is useful for planning algorithms utilizing data from vehicular networks, for example. These algorithms may be robust to some data staleness and this robustness can be used to save energy. Our model relies on the statistical distribution of user queries depending on which we develop sensor sampling schedules while optimizing system cost. For Poisson arrivals of user queries, we develop an optimal data sampling strategy which samples the network at regular intervals. For hyper-exponential query inter arrivals, we discuss methods to find an optimal sampling strategy. We show that optimal strategies can be discovered using dynamic programming techniques but the process is highly computational. Due to this reason, we suggest suboptimal sampling strategies which are nearly as efficient as the optimal strategy. We carefully design the cost function for the sensing system such that it is truly representative of most platforms we want to optimize for. Our model is generic and can be used to model any system that aggregates information which is then queried in real-time by users.
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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 | 0.005 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".