Optimizing the Video Streaming Performance with an Energy-Efficient Evolution of Cub to Predator Based Resource Allocation
Bibliographic record
Abstract
Abstract Wireless sensor networks (WSNs) and video streaming services have gained popularity in recent years. The MPEG-4 H.264/AVC protocol has been widely adopted for video streaming in WSNs, but it is important to transmit sensitive video packets securely to protect the confidentiality of mission-critical applications. It is crucial to ensure that security measures do not negatively impact network performance or video quality. While previous research has focused on secure and energy-efficient video streaming over WSNs, none have addressed the integration of video quality optimization and energy consumption.This study aims to create a more efficient method of encrypting video streaming in wireless sensor networks (WSNs) by combining selective Elliptic Curve Cryptography (ECC) with Evolution of Cub to Predator (ECP). The proposed method improves energy, distortion, and encryption performance by up to 10 dB more than other methods (EEP, UEP (GA), and MCS-RA) through the use of network resource allocation with ECP-RA in WSNs. Quantitative measures such as MSE, PSNR, RMSE, and SSIM demonstrate that this approach is superior to others.
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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 | 0.001 |
| 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) | 0.001 | 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".