An efficient priority packet scheduling algorithm for Wireless Sensor Network
Bibliographic record
Abstract
Scheduling real-time and non-real time packets at the sensor nodes is significantly important to reduce processing overhead, energy consumptions, communications bandwidth, and end-to-end data transmission delay of Wireless Sensor Network (WSN). Most of the existing packet scheduling algorithms of WSN use assignments based on First-Come First-Served (FCFS), non-preemptive priority, and preemptive priority scheduling. However, these algorithms incur a large processing overhead and data transmission delay and are not dynamic to the data traffic changes. In this paper, we propose three-class priority packet scheduling scheme. Emergency real-time packets are placed into the highest priority queue and can preempt the processing of packets at other queues. Other packets are prioritized based on the location of sensor nodes and are placed into two other queues. Lowest priority packets can preempt the processing of their immediate higher priority packets after waiting for a certain number of timeslots. Simulation results show that the proposed three-class priority packet scheduling scheme outperforms FCFS and multi-level queue schedulers in terms of end-to-end data transmission delay.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".