Throughput, Coverage and Scalability of LoRa LPWAN for Internet of Things
Bibliographic record
Abstract
LoRa is a leading Low-Power Wide-Area Network (LPWAN) technology for Internet of Things (IoT). While LoRa networks are rapidly being deployed around the world, it is important to understand the capabilities and limitations of this technology in terms of its throughput, coverage and scalability. Using a combination of real-world measurements and high fidelity simulations, this paper aims at characterizing the performance of LoRa. Specifically, we present and analyze measurement data collected from a city-wide LoRa deployment in order to characterize the throughput and coverage of LoRa. Moreover, using a custom-built simulator tuned based on our measurement data, we present extensive simulation results in order to characterize the scalability of LoRa under a variety of traffic and network settings. Our measurement results show that as few as three gateways are sufficient to cover a dense urban area within an approximately 15 Km radius. Also, a single gateway can support as many as 105end devices, each sending 50 bytes of data every hour with negligible packet drops. On the negative side, while a throughput of up to 5.5 Kbps can be achieved over a single 125 KHz channel at the physical layer, the throughput achieved at the application layer is substantially lower, less than 1 Kbps, due to the network protocols overhead.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".