ACK with Interference Detection Technique for ZigBee Network under Wi-Fi Interference
Bibliographic record
Abstract
Recent studies have shown that the low-power ZigBee based wireless sensor networks (WSN) are vulnerable to the interference of Wi-Fi wireless local area networks (WLAN). From our experimental studies on this coexistence issue, it is found that there are a large number of acknowledgement (ACK) packet losses in the ZigBee packet transmission process, which, in turn, generates large number of unnecessary packet retransmissions, wasting precious channel bandwidth and energy. To address this issue, in this paper, a novel technique named ACK with Interference Detection (ACK-ID) that can effectively reduce the ACK losses and consequently reduce ZigBee packet retransmissions is proposed and implemented in the Crossbow MICAz motes of our testbed. The experimental performance evaluation results show that the proposed ACK-ID can significantly improve the performance of ZigBee packet transmission in terms of ACK delivery rate and packet retransmission rate while operating under interference from the collocated WLAN.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".