SF-Adaptive Duty-Cycled LoRa Networks: Scalability, Reliability, and Latency Tradeoffs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper investigates the performance of adaptive LoRa networks with dynamic SF allocation accounting for Duty Cycle (DC) restrictions and quantifying the imperfect orthogonality of Spreading Factor (SF)s. The study presents a novel spatiotemporal model that combines stochastic geometry and queuing theory where LoRa devices are perceived as interacting two-dimensional DTMCs. Each chain jointly tracks the number of packets in the buffer and the node’s protocol state. Numerical simulations are carried out to validate the accuracy of the proposed model. The network performance is studied in terms of Pareto frontiers under different orthogonality assumptions and adaptation settings, showcasing the ranges of sensing applications that LoRa can accommodate without compromising the network stability. The evolution of SFs activity distribution, coverage probability and average latency is examined against different network parameters. The results show that activating SF adaptation with higher cardinality is not always advantageous and evince the existence of an adaptation cardinality that minimises the delay. The study also identifies regimes where SF adaptation is advantageous for the network scalability and reveals ‘SF-Up’ and ‘SF-Down’ rates that maximise the coverage or minimise the delay. Comparing dynamic to static SF allocations, the results highlight a tradeoff between coverage and latency yielding valuable insights into scenarios where either of the allocation strategies would be more beneficial to the network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it