On‐Demand Mobile Data Collection in Cyber‐Physical Systems
Bibliographic record
Abstract
The collection of sensory data is crucial for cyber‐physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs’ limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on‐demand MDC; that is, MAs collect data based on the real‐time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve ‐based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".