Congestion control for spatio-temporal data in cyber-physical systems
Bibliographic record
Abstract
Data dissemination protocols in cyber-physical systems must consider the importance of data packets in protocol decisions. Importance of data cannot generally be accurately represented by a static priority value or deadline, but rather must stem from the dynamic state of the physical world. This paper presents a novel congestion control scheme for data collection applications that makes two key contributions. First, packet importance is measured by data contributions to the accuracy of estimating the monitored physical phenomenon. This leads to congestion control that minimizes estimation error. Second, our protocol employs a novel mechanism, i.e. spatial aggregation, in addition to temporal aggregation to control congestion. The protocol is generalized to multiple concurrent applications. Our approach employs different granularities of aggregation in transporting spatio-temporal data from nodes to a base station. The aggregation granularity is chosen locally based on the contribution of the transmitted data to the reconstruction of the phenomenon at the receiver. In an area affected by congestion, data are summarized more aggressively to reduce data transfer rate while introducing minimal error to the estimation of physical phenomena. We implement this scheme as a transport layer protocol in LiteOS running on MicaZ motes. Through experiments, we show that the proposed scheme eliminates congestion with an estimation error an order of magnitude smaller than traditional rate control approaches.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".