TA-MAC: Task Aware MAC Protocol for Wireless Sensor Networks
Bibliographic record
Abstract
In wireless sensor networks (WSNs), reducing energy consumption of resource constrained sensor nodes is one of the most important issues. In this paper, we propose a task aware (TA) MAC protocol, which improves energy efficiency and throughput by introducing a channel access scheme depending on traffic load in WSNs. The amount of traffic load of a sensor node can be estimated by its task activity, where a task is an operation that the sensor node performs based on the schedule set by data dissemination procedures in advance. In addition, the sensor node collects neighbor nodes' task activities and determines its channel access probability using the collected information. Consequently, the sensor node can choose a more suitable channel access probability which is adaptive to its traffic load as well as neighbor's traffic load. We carry out performance analysis using a p-persistent MAC protocol. The results reveal that the TA-MAC protocol exhibits less collisions than the normal p-persistent MAC protocol and thus it achieves energy efficient operations. Also, it can been seen that the TA-MAC protocol improves system throughput compared with other protocols
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".