Semi-persistent CSMA/CA for efficient and reliable communication in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are constituted by small, intelligent, and resource-constrained wireless devices (or sensor nodes) that are densely deployed in a certain field to monitor a specific phenomenon. The sensor nodes are constrained in power resources as well as memory and processing capabilities. This fact imposes an essential requirement on the design of WSNs; all algorithms and protocols should be lightweight and conservative in their power consumption. In this paper we aim at researching the opportunity of building a hybrid MAC protocol for beacon-enabled 802.15.4-based WSNs that incorporates some aspects of 802.11 MAC into the operation of 802.15.4 MAC. We will study the impact of increasing the number of Clear Channel Assessments (CCAs) on the performance, and control this increase adaptively to better enhance the efficiency of the network. The main goal is to design a MAC protocol that can respond to the changes in the network (in terms of the size of the network, the intensity of the traffic, or the urgency of the traffic).
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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".