STX-Vote:Improving Reliability with Bit Voting in Synchronous Transmission-based IoT Networks
Bibliographic record
Abstract
Industrial Internet of Things (IIoT) networks must meet strict reliability, latency, and low energy consumption requirements. However, traditional low-power wireless protocols are ineffective in finding a sweet spot for balancing these performance metrics. Recently, network flooding protocols based on Synchronous Transmissions (STX) have been proposed for better performance in reliability-critical IIoT, where simultaneous transmissions are possible without packet collisions. STX-based protocols can offer a competitive edge over routing-based protocols, particularly dependability. However, they notably suffer from the beating effect, a physical layer phenomenon that results in sinusoidal interference across a packet and, consequently, packet loss. Thus, we introduce STX-Vote, an error correction scheme that can handle errors caused by beating effects. Importantly, we utilize transmission redundancy already inherent within STX protocols so do not incur additional on-air overhead. Through simulation, we demonstrate STX-Vote can substantially increase reliability. We subsequently implement STX-Vote on nRF52840-DK devices and perform extensive experiments. The results confirm that STX-Vote improves reliability by 25-28% for BLE 5 PHYs and 8% for IEEE 802.15.4; thus, it can complement existing error correction schemes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".