Deep Learning Based Joint Collision Detection and Spreading Factor Allocation in LoRaWAN
Bibliographic record
Abstract
<p>Long-range wide area network (LoRaWAN) is a promising low-power network standard that allows for longdistance wireless communication with great power saving. LoRa is based on pure ALOHA protocol for channel access, which causes collisions for the transmitted packets. The collisions may occur in two scenarios, namely the intra-spreading factor (intra-SF) and the inter-spreading factor (inter-SF) interference. Consequently, the SFs assignment is a very critical task for the network performance. This paper investigates a smart SFs assignment technique to reduce collisions probability and improve the network performance. In this work, we exploit different architectures of artificial neural networks for detecting collisions and selecting the optimal SF. The results show that the investigated technique achieves a higher prediction accuracy than traditional machine learning algorithms and enhances the energy consumption of the network.</p>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".