A Cluster Based On-demand Multi-Channel MAC Protocol for Wireless Multimedia Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
A Wireless Multimedia Sensor Network (WMSN) is an emerging networking paradigm that allows retrieving video and audio streams, still images, and generic sensing data. Different from conventional wireless sensor networks, a WMSN requires higher network bandwidth and throughput to deliver multimedia contents effectively using energy-constrained devices. In this paper, we propose a clustered on-demand multi-channel MAC protocol (COM-MAQ) to support energy-efficient, high- throughput, and reliable data transmission in WMSNs. The operation of proposed protocol consists of three sessions: request session, scheduling session, and data transmission session. For COM-MAC to achieve high energy efficiency, first, a scheduled multi-channel medium access is used within each cluster so that cluster members can operate in a contention-free manner within both time and frequency domains to avoid collision, idle listening and overhearing. Second, to maximize the network throughput, a traffic-adaptive and QoS-aware scheduling algorithm is executed to dynamically allocate time slots and channels for sensor nodes based on the current data traffic information and QoS requirements. Finally, to enhance transmission reliability, a spectrum-aware ARQ is incorporated to better exploit the unused spectrum for a balance between the reliability and retransmission. Simulation results indicate that COM-MAC can achieve increased network throughput at the cost of a small control and energy overhead.
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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 | 0.001 |
| 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 it