LoRa-Based IoT System for Emergency Assistance and Safety in Mountaineering
Bibliographic record
Abstract
Mountaineering and trekking are outdoor activities that attract thousands of enthusiasts each year. These activities often take place in remote and isolated areas, where medical assistance is scarce, and rescue operations are challenging. When trekkers are injured in such areas, they face significant challenges in accessing help due to the harsh terrain, limited resources, and most notably due to lack of communication infrastructure. In the last century, an average of four people were killed each year on Mount Everest alone, but in the last decade, the number of deaths increased to an average of 6.5 annually. There is a need for an efficient, flexible, and economical solution for safety in mountaineering and other long-distance remote use cases where cellular networks prove ineffective. One of the promising technologies suitable for this application is the LoRa (long range) Network, which is used for communication in isolated areas such as wooded areas (forests) with more minor power consumption. Fast and low-effort localization can potentially increase the chances of saving injured individuals' lives. The proposed system developed a device made of a microcontroller, a Global Positioning System (GPS) module and an accelerometer module to gather trekker data, a LoRa module, and Bluetooth module to transmit data as well as a power supply, and an integrated mobile software application. The system successfully tested the functionality and reliability of an Internet of Things (IoT) network for tracking and alerting purposes, providing a simple, cost-effective system for safety assistance in case of emergencies. The system showed high accuracy in location tracking, long-range communication capability of up to 1 to 2 kilometers, and reliable performance in various environmental conditions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".