Energy-Efficient Distributed Algorithms for Wireless Multimedia Sensor Network Lifetime Extension
Bibliographic record
Abstract
In Wireless Multimedia Sensor Networks (WMSNs), maximizing network lifetime is a critical challenge due to the high energy demands of video processing and communication. Key factors influencing energy consumption include source coding parameters, the reliability and capacity of communication links, and routing strategies. Since the size of compressed multimedia data—particularly video—is inversely related to its visual quality, a trade-off naturally emerges between data volume and user-perceived quality. This paper addresses the problem of determining optimal coding and routing parameters that extend the network’s operational lifespan while maintaining the required visual quality at the sink and adapting to the dynamic nature of wireless links (e.g., fluctuating capacity and reliability). We propose a fully distributed and adaptive solution tailored to WMSNs. Our approach dynamically adjusts link utilization based on real-time reliability assessments and the residual energy of intermediate nodes. This ensures energy-efficient routing under both stable and perturbed network conditions. Extensive simulations demonstrate that our solution can prolong network lifetime by up to 9.76 times in ideal conditions and by approximately 5 times in the presence of network perturbations, while incurring only 0.16% of total battery energy as overhead. Moreover, the approach is shown to be highly responsive to topology changes, confirming its suitability for real-world WMSN deployments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".