Optimum power and rate allocation in video sensor networks
Bibliographic record
Abstract
In a sensor network, each sensor has a limited energy supply. Therefore, it is critical to minimize the power consumed by each sensor to maximize its lifetime. Video sensor networks differ from conventional sensor networks in the fact that video compression at the sensor node consumes a significant amount of power comparable to that used for communication. This poses new challenges, and renders power efficient algorithms developed for sensor networks not suitable for video sensor networks. In this paper, we develop an algorithm for the minimization of the total consumed power by jointly optimizing the encoding power, the transmission power, and the source rate at each sensor node. Furthermore, MAC layer resource allocation is incorporated into the minimization problem. Video signal distortion due to compression, and packet losses in the wireless channel are studied as well. An efficient solution algorithm is developed using Lagrange duality to solve the minimization problem. Through numerical results, the trade-off between allocating power to the encoding process or the transmission process is characterized. Moreover, the interaction between the nodes competing for channel resources, and how this affects their power consumption and distortion levels is studied.
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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 | 0.000 |
| 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".