Understanding LoRaWAN Transmissions in Harsh Environments: A Measurement-Based Campaign Through Unmanned Aerial/Surface Vehicles
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
Along with the ubiquity of various Internet of Things (IoT) applications, data collection in harsh environments with the help of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) appears as a popular approach. However, from a networking and transmission perspective, how unmanned vehicles collaborate with each other and with end IoT devices as well as the propagation characteristics for such data transmissions are less exploited. In this article, we develop a long-range wide-area network (LoRaWAN)-based data collection framework which enables UAV and/or USV-assisted data acquisition in harsh environments. Within this framework, end devices are placed in hard-to-reach locations and data acquisition is performed in an on-demand manner by unmanned vehicles acting as a gateway or peer for end devices (EDs). The framework also includes a step-by-step procedure for data collection and analytics, from parameter configuration to model validation. To reveal the transmission characteristics, we identify three scenarios in a mountainous region in Romania and perform extensive real-life measurements for data collection. Based on the collected datasets, we develop four propagation models and demonstrate that our empirical models outperform the theoretical reference models. Based on the developed empirical models, the coverage of LoRaWAN for any spreading factor (SF) can be estimated with high precision.
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 | 0.001 | 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