Revealing insights for improvements in LoRaWAN in multiple applications scenarios
Bibliographic record
Abstract
We study LoRaWAN's performance when multiple applications are concurrently running over the same LoRaWAN network. We consider applications that generate data packets using a Poisson process, a random distribution, and at periodic intervals. The LoRa PHY layer supports a number of communication settings. However, here we focus on two specific settings: the setting recommended by LoRaWAN and the setting that yields the highest possible data rate in LoRa. Our results demonstrate the following: (i) LoRaWAN favours applications that generate packets at a higher periodic rate, (ii) LoRAWAN does not favour applications that generate packets at a higher rate under Poisson and uniform random distribution, (iii) LoRaWAN's recommended PHY setting demonstrates poor performance, (iv) LoRa's fastest data rate setting outperforms the LoRaWAN recommended setting, and (v) LoRaWAN favours applications that generate packets using uniform random and Poisson distributions over application that generates packet at periodic interval. Our results also hint that using multi-hop communication along with the LoRa's fastest data rate setting can not only increase the setting's coverage, but it may still deliver better performance relative to the LoRaWAN's recommended setting.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".