Network Size Estimation for LoRa-Based Direct-to-Satellite IoT
Bibliographic record
Abstract
The emerging paradigm of Direct-to-Satellite Internet of Things (DtS-IoT) involves Earth surface nodes communicating directly with Low Earth Orbit (LEO) satellites, utilizing standard Low-Power Wide Area Networks (LPWAN) protocols. One of the core challenges faced in this paradigm is scaling the Medium Access Control (MAC) from a limited number of nodes to potentially thousands within the satellite’s coverage area. To address this issue, medium access control schemes can utilize a priori information on the number of nodes the satellite will cover along its orbit. However, developing technically viable solutions for network size estimation that are both precise and accurate remains an open research challenge. This work presents the implementation, parameter selection, and evaluation of the first LoRa/LoRaWAN-compatible network size estimation protocol that leverages the onboard Optimistic Collision Information (OCI) estimator. Our solution, LoRa-OCI (L-OCI), was integrated into FLoRaSat, a C++ discrete-event DtS-IoT simulator that integrates realistic orbital and LoRa/LoRaWAN communication models. Through an extensive simulation campaign, we can determine appropriate LoRa configurations to achieve low root mean square error (RMSE) and low power consumption. Additionally, our results indicate that the approach is relatively insensitive to LoRa parameters when assessing the aggregated throughput of a Slotted ALOHA Game (SAG) protocol throttled by L-OCI.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".