LoRa Network Planning: Gateway Placement and Device Configuration
Bibliographic record
Abstract
LoRa is a leading Low-Power Wide-Area Network technology for IoT applications that require communication over long distances at low power. While there exist several studies on the performance, scalability and security of LoRa networks, the important problem of how to efficiently plan and deploy LoRa networks has not received much attention so far. In this work, we address this problem, which consists of the joint problems of gateway placement, spreading factor assignment, and power allocation. We formulate the problem as a mixed-integer non-linear optimization problem, which can be solved only for small networks. By systematically analyzing the structural properties of the optimal problem, specifically on regularly-structured networks, we develop an approximate algorithm for planning large-scale LoRa networks efficiently. Simulation results are provided to show the behavior and performance of our algorithm in different network scenarios. We have also compared our algorithm with the commonly used ADR algorithm, which shows 15% and 20% improvement in average throughput and energy efficiency of the network, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".