MoT: A Deterministic Latency MAC Protocol for Mission-Critical IoT Applications
Bibliographic record
Abstract
With the growing demand on the IoT market and limited wireless resources, it is essential to fully utilize the available spectrum by improving the total throughput of the network. Many MAC protocols for IoT rely on pure ALOHA-based channel access. Being that these are contention-based the packet collisions cause a massive drop in both the throughput and packet d elivery ratio. The se, proto cols a re unsuitable formission-critical applications which usually req uire long-range communication with guaranteed packet delivery and high throughput. In this paper, we propose a new hybrid scheduling-based protocol MAC on Time (MoT) that guarantees the delivery of all uplink packets in the network and addresses mos t of the importantparameters re qui red by mission-critical a pplica tio.MoT improves the utilization of the bandwidth ca pacity while providing deterministic latency and increased throughput when compared to other IoT MAC protocols. We then designed a simulator for MoT to allow us to compare its performance against that of LoRaWAN. This work provides valuable insight on the performance of both protocols and will aid future research.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".