A Study on the Power Consumption of H.264/AVC-Based Video Sensor Network
Bibliographic record
Abstract
There is an increasing interest in using video sensor networks (VSNs) as an alternative to existing video monitoring/surveillance applications. Due to the limited amount of energy resources available in VSNs, power consumption efficiency is one of the most important design challenges in VSNs. Video encoding contributes to a significant portion of the overall power consumption at the VSN nodes. In this regard, the encoding parameter settings used at each node determine the coding complexity and bitrate of the video. This, in turn, determines the encoding and transmission power consumption of the node and the VSN overall. Therefore, in order to calculate the nodes’ power consumption, we need to be able to estimate the coding complexity and bitrate of the video. In this paper, we modeled the coding complexity and bitrate of the H.264/AVC encoder, based on the encoding parameter settings used. We also propose a method to reduce the model estimation error for videos whose content changes within a specified period of time. We have conducted our experiments using a large video dataset captured from real-life applications in the analysis. Using the proposed model, we show how to estimate the VSN power consumption for a given topology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".