Throughput, Coverage and Scalability of LoRa LPWAN for Internet of Things
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
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 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> end 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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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 | 0.000 |
| 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