On Enhancing Technology Coexistence in the IoT Era: ZigBee and 802.11 Case
Why this work is in the frame
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Bibliographic record
Abstract
ZigBee is often chosen as a technology to connect things because of characteristics, such as network resilience, interoperability, and low power consumption. In addition, Zigbee Pro, with its Green Power feature, allows low-power networking capable of supporting more than 64 000 devices on a single network, making it an excellent choice to connect things. However, in recent years, we have witnessed the proliferation of smart devices using either 802.11 or ZigBee technologies, which operate in the same frequency band. Proposing and developing techniques that may improve the fair operation and performance of these technologies in coexistence scenarios have been a major concern in industry and academia. In this paper, we propose the use of traffic prioritization for ZigBee nodes in order to improve their performance when coexisting with IEEE 802.11 nodes. We develop an analytical model based on Markov chains, which captures the behavior of channel access mechanisms for both 802.11 nodes and different ZigBee priority class nodes. Based on extensive simulations, we validate the accuracy of the proposed model, and demonstrate how traffic prioritization of ZigBee nodes effectively improves their performance when coexisting with 802.11 nodes. We also demonstrate that this improvement comes at the cost of negligible degradation in the performance of the 802.11 nodes.
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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 | 0.000 |
| 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 it