On Enhancing Technology Coexistence in the IoT Era: ZigBee and 802.11 Case
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".