Cluster-based Distributed Compressed Sensing for QoS Routing in Cognitive Video Sensor Networks
Bibliographic record
Abstract
Compressed sensing based in-network compression methods to minimize the data redundancy are critical to cognitive video sensor networks (CVSNs). However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service (QoS) satisfaction. CDCS, a cluster-based distributed compressed sensing approach for QoS routing is proposed to efficiently deliver visual information in CVSNs. To begin with, the correlation among adjacent video sensors is utilized to determine the suitable set of video sensors that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurement have been obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold, thereby maximizing the removal of redundant traffic without sacrificing video quality. Lastly, the compressed data is transmitted by a distributed QoS routing scheme, with an objective to minimize energy consumption subject to delay and reliability constraints. Simulation results demonstrate that compared with exiting QoS routing schemes, CDCS can achieve energy-efficient data delivery and reconstruction accuracy of visual information.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".