A Cluster Based On-demand Multi-Channel MAC Protocol for Wireless Multimedia Sensor Networks
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".