Content Relevance Opportunistic Routing for Wireless Multimedia Sensor Networks
Bibliographic record
Abstract
Wireless Multimedia Sensor Networks (WMSNs) are considered as one of the most prominent infrastructures for human-centric multimedia applications due to the wide availability of low-cost hardware such as microphones and CMOS cameras. By virtue of the energy limitations on sensor nodes alongside the explicit highly demanding bandwidth requirements of real-time multimedia applications, these particular networks foster a set of non-trivial challenges that need to be confronted. In this paper we define a level of relevance in regards with the content of a multimedia packet and we further introduce a dynamic routing protocol that optimizes the overall network performance in terms of energy efficiency and packet delay. We present the design, implementation and applicability of our Content Relevance Opportunistic Routing (CROR) protocol under experimental results that show an increase in network lifetime of up to 20% compared with traditional routing.
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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 | 0.001 | 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 | 0.001 | 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".